Under Review | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2004 | Preprint

Under Review / In Preparation

B. G. Weinstein, S. Marconi, A. Zare, S. A. Bohlman, A. Singh, S. J. Graves, L. Magee, D. J. Johnson, S. Record, V. E. Rubio, N. G. Swenson, P. Townsend, T. T. Veblen, R. A. Andrus, E. P. White “Individual tree crown maps for the National Ecological Observatory Network”, doi: 10.1101/2023.10.25.563626, Under Review

2024

A. K. Sahoo, H. Geldimuradov, A. Zygala, Y. Cui, M. Lotfollahi, K. Gong, K. Smith, A. Zare, and S. M. Weisberg, Deep learning approaches to associate neural structure with spatial navigation function. Poster presentation at the 65th Annual Meeting of the Psychonomic Society. New York, NY. 2024.

R. Gupta, S. K. Pothapragada, W. Xu, P. K. Goel, M. A. Barrera, M. S. Saldanha, J. B. Harley, K. T. Morgan, A. Zare, and L. Zotarelli, “Estimating soil mineral nitrogen from data-sparse field experiments using crop model-guided deep learning approach,” Computers and Electronics in Agriculture, vol. 225, p. 109355, 2024.

Z. Tian, J. Upchurch, G. A. Simon, J. Dubeux, A. Zare, C. Zhao, and J. B. Harley, “Quantifying Heterogeneous Ecosystem Services with Multi-Label Soft Classification,” in IGARSS 2024 – 2024 IEEE International Geoscience and Remote Sensing Symposium, pp. 427–431, 2024.

Y. Song, G. Sapes, S. Chang, R. Chowdhry, T. Mejia, A. Hampton, S. Kucharski, T. Sazzad, Y. Zhang, S Koppal, and A. Zare, “Hyperspectral signals in the soil: plant-soil hydraulic connections as mechanisms of drought tolerance and rapid recovery,” in Plant, Cell & Environment, 2024.

B. G. Weinstein, S. Marconi, A. Zare, S. A. Bohlman, A. Singh, S. J. Graves, L. Magee, D. J. Johnson, S. Record, V. E. Rubio, and others, “Individual canopy tree species maps for the National Ecological Observatory Network,” PLOS Biology, vol. 22, no. 7, p. e3002700, 2024.

S. Meerdink, D. Hiatt, S. Flory, A. Zare, “Dealing with imperfect data for invasive species detection using multispectral imagery,” in Ecological informatics, vol. 79, p. 102432, 2024, doi: doi.org/10.1016/j.ecoinf.2023.102432.

R. Castillo-Argaez, G. Sapes, N. Mallen, A. Lippert, G. John, A. Zare and W. Hammond, “Spectral ecophysiology: hyperspectral pressure–volume curves to estimate leaf turgor loss,” in New Phytologist, vol. 242, no. 3, pp. 935-946, 2024, doi: 10.1111/nph.19669.

X. Guo, Q. Li, S. Morrison-Smith, L. Anthony, A. Zare, Y. Song, “Elicitating Challenges and User Needs Associated with Annotation Software for Plant Phenotyping,” in Proceedings of the 29th International Conference on Intelligent User Interfaces, pp. 431-443, 2024, doi: doi.org/10.1145/3640543.3645178.

2023

M. Cook, A. Khoury, A. Zare and P. Gader, “Null Space Analysis for Detecting Unknown Objects During Automatic Target Recognition Tasks in Sonar Data,” OCEANS 2023 – MTS/IEEE U.S. Gulf Coast, Biloxi, MS, USA, 2023, pp. 1-5, doi: 10.23919/OCEANS52994.2023.10337218.

C. H. McCurley and A. Zare, “Segmentation Pseudo-label Generation using the Multiple Instance Learning Choquet Integral,” in IEEE Transactions on Fuzzy Systems, doi: 10.1109/TFUZZ.2023.3338049.

S. Graves, S. Marconi, D. Stewart, I. Harmon, B. G. Weinstein, Y. Kanazawa, V. M Scholl, M. B. Joseph, J. McClinchy, L. Browne, M. K. Sullivan, S. Estrada-Villegas, D. Zhe Wang, A. Singh, S. A. Bohlman, A. Zare, E. P. White, “Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data.” PeerJ, 2023, doi: 10.7717/peerj.16578.

X. Guo, C. Tseung, A. Zare, T. Liu. “Hyperspectral image analysis for the evaluation of chilling injury in avocado fruit during cold storage,” Postharvest Biology and Technology, vol. 206, p.112558, ISSN 0925-5214, 2023.

B. G. Weinstein, S. Marconi, S. J. Graves, A. Zare, A. Singh, S. A. Bohlman, L. Magee, D. J. Johnson, P. A. Townsend, E. P. White. “Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model.” Remote Sensing in Ecology and Conservation, vol. 9, pp. 656-670, 2023, doi: 10.1002/rse2.335.

D. Stewart, A. Kreulach, S. F. Johnson and A. Zare, “Image-to-Height Domain Translation for Synthetic Aperture Sonar,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-13, 2023, Art no. 4201113, doi: 10.1109/TGRS.2023.3236473.

J. Peeples, W. Xu, R. Gloaguen, D. Rowland, A. Zare, and Z. Brym, “Spatial and Texture Analysis of Root System distribution with Earth mover’s Distance (STARSEED),” in Plant Methods, vol. 19, pp. 1-15, ISSN 1746-4811. 2023, Art no. 2, doi: 10.1186/s13007-022-00974-z.

2022

X. Guo, Y. Ahlawat, A. Zare and T. Liu, “Evaluation of Postharvest Senescence in Broccoli via Hyperspectral Imaging.” Plant Phenomics. 2022. doi: 10.34133/2022/9761095.

A. Starke, K. Nagaraj, C. Ruben, N. Aljohani, S. Zou, A. Bretas, J. McNair and A. Zare, “Cross-Layered Distributed Data-Driven Framework for Enhanced Smart Grid Cyber-Physical Security.” in IET Smart Grid. vol. 5. issue 6. pages 398-416. ISSN 2515-2947. 2022.

I. Harmon, S. Marconi, B. Weinstein, S. Graves, D.Z. Wang, S. Bohlman, A. Zare, A. Singh, and E. White, “Injecting Domain Knowledge Into Deep Neural Networks for Tree Crown Delineation,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-19, 2022, Art no. 4415419, doi: 10.1109/TGRS.2022.3216622.

A. Dutt, A. Zare and P. Gader, “Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion With Missing Data,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 9439-9456, 2022, doi: 10.1109/JSTARS.2022.3217485.

C. McCurley, “Discriminative Feature Learning with Imprecise, Uncertain, and Ambiguous Data,” Ph.D Thesis, Gainesville, FL, 2022.

J. Peeples, “Connecting the Past and the Present : Histogram Layers for Texture Analysis,” Ph.D Thesis, Gainesville, FL, 2022.

D. Stewart, “Domain Translation and Image Registration for Multi-Look Synthetic Aperture Sonar Scene Understanding,” Ph.D Thesis, Gainesville, FL, 2022.

R. Fick, “Robust GANs for Semi-Supervised Classification,” Ph.D Thesis, Gainesville, FL, 2022.

S. Marconi, B.G. Weinstein, S. Zou, S.A. Bohlman, A. Zare, A. Singh, D. Stewart, I. Harmon, A. Steinkraus, and E.P. White, “Continental-scale hyperspectral tree species classification in the United States National Ecological Observatory Network.” Remote Sensing of Environment, vol. 282, p.113264., ISSN 0034-4257, 2022.

C. McCurley, D. Rodriguez, C. Trousdale, A. Stevens, A. Baldino, E. Li, I. Perlmutter, and A. Zare “Bag-level classification network for infrared target detection”, in Proc. SPIE 12096, Automatic Target Recognition XXXII, 1209603, doi: 10.1117/12.2618325, 2022.

Maggie M. Hantak, Robert P. Guralnick, A. Zare, Brian J. Stucky, “Computer vision for assessing species color pattern variation from web-based community science images”, pp. 104784, ISSN 2589-0042, https://doi.org/10.1016/j.isci.2022.104784, in iScience, 2022.

J. Peeples, J. Jameson, N. Kotta, J. Grasman, W. Stoppel, and A. Zare, “Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation” in BME Frontiers, vol. 2022, doi: 10.34133/2022/9854084.

S.K. Meerdink, J. Bocinsky, A. Zare, N. Kroeger, C. H. McCurley, D. Shats and P.D. Gader, “Multi-Target Multiple Instance Learning for Hyperspectral Target Detection,” in IEEE Transaction on Geoscience and Remote Sensing (TGRS), vol. 60, pp. 1-14, Art no. 5502814, doi: 10.1109/TGRS.2021.3060966, 2022.

J. Peeples, S. Walker, C. McCurley, A. Zare, J. Keller and and W. Xu, “Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification.”, in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 3511305, doi: 10.1109/LGRS.2022.3156532.

W. Xu, G. Yu, Y. Cui, R. Gloaguen, A. Zare, J. Bonnette, J. Reyes-Cabrera, A. Rajurkar, D. Rowland, R. Matamala, J. Jastrow, T. Juenger, and F. Fritschi. “PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study.” in AI for Agriculture and Food Systems (AIAFS)Workshops at the AAAI conference on artificial intelligence. February, 2022.

J. Peeples, C. McCurley, S. Walker, D. Stewart and A. Zare, “Learnable Adaptive Cosine Estimator (LACE) for Image Classification.” in WACV, 2022.

2021

G. Yu, “Weakly Supervised Image Segmentation with Multiple Instance Learning Neural Network,” Ph.D. Thesis, Gainesville, FL, 2021.

J. Peeples, W. Xu and A. Zare, “Histogram Layers for Texture Analysis.” in IEEE Transactions on Artificial Intelligence, DOI 10.1109/TAI.2021.3135804, 2021.

D. Stewart, A. Zare, S. Marconi, B. Weinstein, E. White, S. Graves, S. Bohlman and A. Singh, “RandCrowns: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation.”, vol. 14, pp. 11229-11239, DOI 10.1109/JSTARS.2021.3122345, 2021.

T. Zou, N. Aljohani, K. Nagaraj, S. Zou, C. Ruben, A. Bretas, A. Zare and J. McNair, “A Network Parameter Database FDI Correction Physics-based Model: A Machine Learning Synthetic Measurement based Approach.” in Applied Sciences Journal, vol. 11, 2021.

V. R. Merenda, J. Ruiz-Munoz, A. Zare and R. C. Chebel, “Predictive models to identify Holstein cows at risk of metritis and clinical cure and reproductive/productive failure following antimicrobial treatment,” in Preventive Veterinary Medicine, vol. 194, pp. 105431, 2021.

B. Weinstein, S. Graves, S. Marconi, A. Singh, A. Zare, D. Stewart, S. Bohlman and E. P. White, “A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network,” in PLOS Computational Biology, vol. 2, pp. 1-18, DOI 10.1371/journal.pcbi.1009180, 2021.

S. Zou, “Classification with Multi-Imprecise Labels,” Ph.D. Thesis, Gainesville, FL, 2021.

C. Jiao, C. Chen, S. Gou, D. Hai, B. Su, M. Skubic, L. Jiao, A. Zare and K.C. Ho, “Non-Invasive Heart Rate Estimation from Ballistocardiograms using Bidirectional LSTM Regression,” in IEEE Journal of Biomedical and Health Informatics, 2021.

R. A. Borsoi, T. Imbiriba, J.C.M. Bermudez, C. Richard, J. Chanussot, L. Drumetz, J. Tourneret, A. Zare and C. Jutten, “Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review,” in IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 4, pp. 223-270, Dec. 2021, doi: 10.1109/MGRS.2021.3071158.

D. Prioleau, K. Alikhademi, A. Roberts, J. Peeples, A. Zare and J. Gilbert, “Application of Divisive Clustering for Reducing Bias in Imbalanced Data,” in 2021 International Conference on Machine Learning and Data Mining (MLDM), P-ISSN 1864-9734, E-ISSN 2699-5220, ISBN 978-3-942952-81-1, pp. 115-129, 2021.

D. Stewart, A. Hampton, A. Zare, J. Dale and J. Keller, “The Weakly-Labeled Rand Index,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2313-2316, doi: 10.1109/IGARSS47720.2021.9553182, 2021.

S. Walker, J. Peeples, J. Dale, J. Keller and A. Zare, “Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2835-2838, doi: 10.1109/IGARSS47720.2021.9554901, 2021.

K. Nagaraj, N. Aljohani, S. Zou, T. Zou, A. Bretas, J. McNair and A. Zare, “Smart FDI Attack Design and Detection with Data Transmutation Framework for Smart Grids,” in 2021 IEEE PES General Meeting, July 25-29, 2021, Washington, DC, USA.

B. G. Weinstein, S. Marconi, S. A. Bohlman, A. Zare, A. Singh, S. J. Graves and E. P. White. “A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network,” in eLife, 2021.

K. Nagaraj, N. Aljohani, S. Zou, C. Ruben,  A. Bretas, A. Zare and J. McNair, “State Estimator and Machine Learning Analysis of Residual Differences to Detect and Identify FDI and Parameter Errors in Smart Grids,” in 2020 North American Power Symposium (NAPS), 2021.

2020

C. Jiao, X. Du, A. Zare, “Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis.” in: Prasad, S., Chanussot, J. (eds) Hyperspectral Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, Cham, 2020.

G. Yu, A. Zare, W. Xu, R. Matamala, J. Reyes-Cabrera, F. B. Fritschi and T. E. Juenger, “Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM,” in the 16th European Conference on Computer Vision (ECCV) Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP 2020), 2020.

A. Seethepalli, H. Guo, X. Liu, M. Griffiths, H. Almtarfi, Z. Li, S. Liu, A. Zare, F. Fritschi, E. Blancaflor, X. Ma, and L. York, “RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping,” in Plant Phenomics,vol. 2020, Article ID 3074916, 15 pages, 2020.

K. Nagaraj, S. Zou, C. Ruben, S. C. Dhulipala, A. Starke, A. Bretas, A. Zare and J. McNair, “Ensemble CorrDet with Adaptive Statistics for Bad Data Detection,” in IET Smart Grid.  pp. 572 – 580, vol. 3, 2020.

C. A. Smith, Y. Cui, B. D. Miler, D. D. Keiser, A. Zare and A. Aitkaliyeva, “New approach for measuring interconnectivity of fission gas pores in nuclear fuels from 2D micrographs,” in Journal of Materials Science, 2020.

M. Cook, A. Zare and P. Gader, “Outlier Detection through Null Space Analysis of Neural Networks,” presented at the 37th International Conference on Machine Learning (ICML 2020) Workshop on Uncertainty and Robustness in Deep Learning (UDL), Virtual Conference, 2020.

J.F. Ruiz-Munoz, J.K. Nimmagadda, T.G. Dowd, J.E. Baciak and A. Zare, “Super Resolution for Root Imaging,” in Applications in Plant Sciences (APPS), vol. 8, num. 7, pp. e11374, 2020.

P. S. Soltis,  G. Nelson, A. Zare, and E. K. Meineke, “Plants meet machines: Prospects in machine learning for plant biology,” in Applications in Plant Sciences, vol. 8, num. 6, pp. e11371, 2020.

G. Yu, A. Zare, H. Sheng, R. Matamala, J. Reyes-Cabrera, F. B. Fritschi and T.E. Juenger, “Root Identification in Minirhizotron Imagery with Multiple Instance Learning,” in Machine Vision and Applications, vol. 31, 2020.

W. Xu, G. Yu, A. Zare, B. Zurweller, D. Rowland, J. Reyes-Cabrera, F. Fritschi, R. Matamala and T. Juenger, “Overcoming Small Minirhizotron Datasets Using Transfer Learning,” in Computers and Electronics in Agriculture, vol. 175, 2020.

B. Weinstein, S. Marconi, S. Bohlman, A. Zare, E. White, “Cross-site learning in deep learning RGB tree crown detection,” in Ecological Informatics, vol. 56, 2020.

X. Du and A. Zare, “Multi-resolution Multi-modal Sensor Fusion For Remote Sensing Data with Label Uncertainty,” in IEEE Trans. on Geoscience and Remote Sensing (TGRS), vol. 58, no. 4, pp. 2755-2769, April 2020.

2019

H. Sheng, “Switchgrass Genotype Classification using Hyperspectral Imagery,” M.Sc. Thesis, Gainesville, FL, 2019.

P. Lyons, “Anomaly and Target Detection in Synthetic Aperture Sonar,” M.Sc. Thesis, Gainesville, FL, 2019.

C. Ruben, S. Dhulipala, K. Nagaraj, S. Zou. A. Starke, A. Bretas, A. Zare, and J. McNair, “Hybrid data-driven physics model-based framework for enhanced cyber-physical smart grid security,” in IET Smart Grid Special Issue: Machine Learning in Power Systems. Dec 2019.

X. Du, A. Zare and D. Anderson, “Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels,” The 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI). Xiamen, China. Dec. 2019. pp. 1154-1162.

S. Meerdink, J. Bocinsky, E. Wetherley, A. Zare, C. McCurley, and P. Gader, “Developing Spectral Libraries Using Multiple Target Multiple Instance Adaptive Cosine/Coherence Estimator,”  10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1-5, Sept. 2019.

R. Fick, P. Gader, A. Zare, and S. Meerdink, “Temporal mapping of hyperspectral data”, 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1-4, Sept. 2019.

S. Zou, Y. Tseng, A. Zare, D. Rowland, B. Tillman and S. Yoon, “Peanut Maturity Classification using Hyperspectral Imagery,” in Biosystems Engineering, vol. 188, pp. 165-177, 2019.

S. K. Meerdink, D. A. Roberts, K. L. Roth, J. Y. King, P. D. Gader, and A. Koltunov, “Classifying California plant species temporally using airborne hyperspectral imagery,” in Remote Sensing of Environment, vol. 232 (111308), Oct 2019.

S. K. Meerdink, S. J. Hook, D. A. Roberts, and E. A. Abbott, “The ECOSTRESS spectral library version 1.0,” in Remote Sensing of Environment, vol. 230 (111196), Sept. 2019.

R. H. J. Heim, A. J. Carnegie, and P. J. Zarco-Tejada, “Breaking down barriers between remote sensing and plant pathology,” in Tropical Plant Pathology, vol. 44, pp. 398–400, July 2019.

S. Meerdink,  D. Roberts, G. Hulley, P. Gader,  J. Pisek, K. Adamson, and S. J. Hook,  “Plant species’ spectral emissivity and temperature using the hyperspectral thermal emission spectrometer (HyTES) sensor,” in Remote Sensing of Environment, vol. 224, pp. 421–435, Apr 2019.

B. Weinstein, S. Marconi, S. Bohlman, A. Zare and E. White, “Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks,” in Remote Sensing. 2019. vol 11. issue 11. pp 1309. 

A. Galusha, J. Keller, A. Zare, “Deep Convolutional Neural Network Target Classification for Under- water Synthetic Aperture Sonar Imagery,” SPIE Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, Baltimore, MD, April 2019.

J. Dale, A. Galusha, J. Keller, A. Zare, “Evaluation of Image Features for Discriminating Targets from False Positives in Synthetic Aperture Sonar Imagery,” SPIE Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, Baltimore, MD, April 2019.

J. Bocinsky, “Learning Multiple Target Concepts from Uncertain, Ambiguous Data Using the Adaptive Cosine Estimator and Spectral Match Filter,” M.Sc. Thesis, Gainesville, FL, 2019.

J. Bocinsky, C. McCurley, D. Shats and A. Zare, “Investigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator,” in Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 2019.

J. Peeples, M. Cook, D. Suen, A. Zare and J. Keller, “Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation,” in Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 2019.

C. McCurley, J. Bocinsky, A. Zare, “Comparison of Hand-held WEMI Target Detection Algorithms,” in Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 2019.

X. Du and A. Zare, “Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 2741-2753, May 2019.

S. Zou, P. Gader and A. Zare, “Hyperspectral Tree Crown Classification Using the Multiple Instance Adaptive Cosine Estimator,” PeerJ 7:e6405, Feb. 2019.

2018

S. Avusali, “Three Dimensional Reconstruction of Plant Roots via Low Energy X-ray Computed Tomography,” M.Sc. Thesis, Columbia, MO, 2018.

W. Chen, S. Gou, X. Wang, L. Jiao, C. Jiao, A. Zare, “Complex Scene Classification of PoLSAR Imagery Based on a Self-Paced Learning Approach,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, pp. 4818 – 4825, Dec. 2018.

C. Jiao, C. Chen, R. McGarvey, S. Bohlman, L. Jiao and A. Zare, “Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection,”ISPRS Journal of Photogrammetry and Remote Sensing, vol. 146, pp. 235 – 250, Dec. 2018.

T. Liu, A. Abd-Elrahman, A. Zare, B. Dewitt, L. Flory, and S. Smith “A Fully Learnable Context-driven Object-based Model for Mapping Land Cover Using Multi-view Data from Unmanned Aircraft Systems,” Remote Sensing of Environment, vol. 216, pp. 328-344, Oct. 2018.

A. Starke, J. McNair, R. Trevizan, A. Bretas, J. Peeples, and A. Zare “Toward Resilient Smart Grid Communications Using Distributed SDN with ML-based Anomaly Detection,” in The 16th International Conference on Wired/Wireless Internet Communications, Boston, MA, Jun. 2018.

S. Zou, H. Sun, and A. Zare “Hyperspectral Unmixing with Endmember Variability Using Semi-supervised Partial Membership Latent Dirichlet Allocation,” in American Association of Geographers, New Orleans, LA, Apr. 2018. Abstract and Presentation Only.

D. Stewart, A. Zare, and J. T. Cobb, “Quantitative Evaluation Metrics for Superpixel Segmentation” in Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.

J. Baciak, S. Cui, H. Gardiner, S. Kovaleski, and A. Zare, “Use of Pencil Beam Backscatter Radiography to Image Buried Root Structures,” in Symp. on Radiation Measurements and Applications XVII, Ann Arbor, MI, June 2018.

T. Nabelek, J. Keller, A. Zare, “Fractal Analysis of Seafloor Textures for Target Detection in Synthetic Aperture Sonar Imagery,” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.

A. Galusha, G. Galusha, J. Keller, A. Zare, “A Target Classification Algorithm for Underwater Synthetic Aperture Sonar Imagery,” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.

B. Alvey, D. Ho, A. Zare, “Sample Spacing Variations on the Feature Performance for Subsurface Object Detection Using Handheld Ground Penetrating Radar,” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.

P. Lyons, D. Suen, A. Galusha, A. Zare, and J. Keller, “Comparison of Prescreening Algorithms for Target Detection in Synthetic Aperture Sonar Imagery,” in Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.

J. Peeples, D. Suen, A. Zare, and J. Keller, “Possibilistic Fuzzy Local Information C-means with Automated Feature Selection for Seafloor Segmentation,” in Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.

C. Jiao, B. Su, P. Lyons, A. Zare, K. C. Ho and M. Skubic, “Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms,” IEEE Trans. Biomed. Eng., vol. 65, pp. 2634 – 2648, Nov. 2018.

A. Zare, C. Jiao, and T. Glenn, “Discriminative Multiple Instance Hyperspectral Target Characterization,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, pp. 2342 – 2354, Oct. 2018.

2017

X. Du, “Multiple Instance Choquet Integral For Multi-Resolution Sensor Fusion,” Ph.D. Thesis, Columbia, MO, 2017.

C. Jiao, “Target Concept Learning From Ambiguously Labeled Data,” Ph.D. Thesis, Columbia, MO, 2017.

A. Zare, N. Young, D. Suen, T. Nabelek, A. Galusha, J. Keller, “Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation,” in IEEE Symp. Series on Computational Intelli., Honolulu, Hi, 2017.

C. Chen, A. Zare, H. Trinh, G. Omotara, J. T. Cobb, and P. Lagaunne, “Partial Membership Latent Dirichlet Allocation,” IEEE Trans. Image Process., vol. 26, pp. 5590-5602, Dec. 2017.

A. J. Pinar, D. T. Anderson, T. C. Havens, A. Zare and T. Adeyeba, “Measures of the Shapley Index for Learning Lower Complexity Fuzzy Integrals” Granular Computing (2017): 1-17

R. E. Smith, D. T. Anderson, J. E. Ball, A. Zare and B. Alvey, “Aggregation of Choquet Integrals in GPR and EMI for Handheld Platform-based Explosive Hazard Detection” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.

S. Harris, B. Alvey, D. Ho and A. Zare, “LBP Features for Hand-Held Ground Penetrating Radar” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.

B. Alvey, A. Zare and D. Ho, “Fourier Features for Explosive Hazard Detection Using a Wideband Electromagnetic Induction Sensor” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.

D. T. Anderson, M. A. Islam, R. King, N. H. Younan, J. R. Fairley, S. Howington, F. Petry, P. Elmore and A. Zare, “Binary Fuzzy Measures and Choquet Integration for Multi-Source Fusion” Intl. Conf. on Military Technologies, 2017.

R. Smith, D. Anderson, A. Zare, J. Ball, B. Alvey, J. Fairley, and S. Howington, “Genetic Programming Based Choquet Integral for Multi-Source Fusion ” in IEEE Int. Conf. Fuzzy Syst. , Naples, Italy, 2017, pp. 1-8.

X. Du, A. Seethepalli, H. Sun, A. Zare and J. T. Cobb, “Environmentally-Adaptive Target Recognition for SAS Imagery ” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.

J. T. Cobb, X. Du, A. Zare, and M. Emigh, “Multiple-instance Learning-based Sonar Image Classification ” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.

C. Jiao and A. Zare, “Multiple Instance Hybrid Estimator for Learning Target Signatures” in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS), Fort Worth, TX, 2017

H. Sun and A. Zare, “Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps” in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS), Fort Worth, TX, 2017

S. Zou and A. Zare, “Hyperspectral Unmixing with Endmember Variability Using Partial Membership Latent Dirichlet Allocation.” in IEEE Int. Conf. Acoust, Speech and Signal Process. (ICASSP), New Orleans, LA, 2017, pp. 6200-6204.

S. Zou, H. Sun, and A. Zare, “Hyperspectral Unmixing with Endmember Variability Using Semi-supervised Partial Membership Latent Dirichlet Allocation,” in CoRR. vol abs/1703.06151. 2017.  

2016

A. Seethepalli, “Multi-camera High-throughput Plant Root Phenotyping System,” M.Sc. Thesis, Columbia, MO, 2016.

H. Sun, “Map-guided Hyperspectral Image Superpixel Segmentation Using Semi-supervised Partial Membership Latent Dirichlet Allocation,” M.Sc. Thesis, Columbia, MO, 2016.

S. Zou, “Semi-supervised Interactive Unmixing for Hyperspectral Image Analysis,” M.Sc. Thesis, Columbia, MO, 2016.

B. Alvey, A. Zare, M. Cook, and D. Ho, “Adaptive Coherence Estimator (ACE) for Explosive Hazard Detection Using Wideband Electromagnetic Induction (WEMI),” in Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 2016.

J. Bezdek, X. Ye, M. Popescu, J. Keller and A. Zare, “Random Projection Below the JL Limit,” 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 2016, pp. 2414-2423.

C. Chen, “Partial Membership Latent Dirichlet Allocation,” PhD Thesis, Columbia, MO, 2016.

C. Chen, A. Zare, and J. T. Cobb, “Partial Membership Latent Dirichlet Allocation for Image Segmentation,” in Int. Conf. Pattern Recognition (ICPR), 2016.

M. Cook, A. Zare, and D. Ho, “Buried Object Detection Using Handheld WEMI with Task-driven Extended Functions of Multiple Instances,” in Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 2016.

X. Du, A. Zare, J. Keller, and D. Anderson, “Multiple Instance Choquet Integral for Classifier Fusion,” in IEEE Congr. Evol. Computation (CEC), Vancouver, BC, 2016, pp. 1054-1061.

S. Harris, D. Ho, and A. Zare, “On the Use of Log-gabor Features for Subsurface Object Detection Using Ground Penetrating radar,” in Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 2016.

R. Heylen, P. Scheunders, A. Zare, and P. Gader, “Alternating Angle Minimization Based Unmixing with Endmember Variability,” in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS), Beijing, 2016, pp. 6974-6977.

R. Heylen, A. Zare, P. Gader, and P. Scheunders, “Hyperspectral Unmixing With Endmember Variability via Alternating Angle Minimization,” IEEE Trans. Geosci. Remote Sens., vol. 54, pp. 4983-4993, Aug. 2016.

C. Jiao, P. Lyons, A. Zare, L. Rosales, and M. Skubic, “Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances,” in Proc. EMBC, 2016.

C. Jiao and A. Zare, “Multiple Instance Dictionary Learning using Functions of Multiple Instances,” in Int. Conf. Pattern Recognition (ICPR), 2016.

S. Zou and A. Zare, “Instance Influence Estimation for Hyperspectral Target Signature Characterization Using Extended Functions of Multiple Instances,” in Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 2016.

2015

C. Chen, A. Zare, and J. T. Cobb, “Sand Ripple Characterization Using an Extended Synthetic Aperture Sonar Model and Parallel Sampling Method,” IEEE Trans. Geosci. Remote Sens., vol. 53, pp. 5547-5559, Oct. 2015.

M. Cook, “Task Driven Extended Functions of Multiple Instances,” Master Thesis, Columbia, MO, 2015.

X. Du, A. Zare, and J. T. Cobb, “Possibilistic context identification for SAS imagery,” in Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 2015.

T. C. Glenn, A. Zare, and P. Gader, “Bayesian Fuzzy Clustering,” IEEE Trans. Fuzzy Syst., vol. 23, pp. 1545-1561, Oct. 2015.

T. C. Glenn and A. Zare, “Estimating Target Signatures with Diverse Density,” in 7th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015.

K. C. Ho, S. Harris, A. Zare, and M. Cook, “Anomaly detection of subsurface objects using handheld ground-penetrating radar,” in Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 2015.

C. Jiao and A. Zare, “Functions of Multiple Instances for Learning Target Signatures,” IEEE Trans. Geosci. Remote Sens., vol. 53, pp. 4670-4686, Aug. 2015.

M. Popescu, J. Keller, J. Bezdek, and A. Zare, “Random Projections Fuzzy C-means (RPFCM) for Big Data Clustering,” in IEEE Int. Conf. Fuzzy Syst. (FUZZ-IEEE), Istanbul, 2015, pp. 1-6.

A. Zare and C. Jiao, “Functions of Multiple Instances for Sub-pixel Target Characterization in Hyperspectral Imagery,” in Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 2015.

A. Zare, M. Cook, B. Alvey, and D. Ho, “Multiple Instance Dictionary Learning for Subsurface Object Detection Using Handheld EMI,” in Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 2015.

2014

A. Buck, A. Zare, J. Keller, and M. Popescu, “Endmember Representation of Human Geography Layers,” in IEEE Symp. Computational Intell. Big Data (CIBD), Orlando, FL, 2014, pp. 1-6.

C. Chen, A. Zare, and J. T. Cobb, “Invariant Parameter Estimation Across Varying Seabeds in Synthetic Aperture Sonar Imagery,” in 3rd Int. Conf. on SAS and SAR, 2014.

J. T. Cobb and A. Zare, “Boundary Detection and Superpixel Formation in Synthetic Aperture Sonar Imagery,” in 3rd Int. Conf. SAS and SAR, 2014.

X. Du, A. Zare, P. Gader, and D. Dranishnikov, “Spatial and Spectral Unmixing Using the Beta Compositional Model,” IEEE J. Sel. Topics. Appl. Earth Observ., vol. 7, pp. 1994-2003, June, 2014.

J. Keller, A. Buck, A. Zare, and M. Popescu, “A Human Geospatial Predictive Analytics Framework with Application to Finding Medically Underserved Areas,” in IEEE Symp. Computational Intell. Big Data (CIBD), Orlando, FL, 2014, pp. 1-6.

P. Khopkar, “Hyperspectral Unmixing and Band Weighting for Multiple Endmember Sets,” Master Thesis, Columbia, MO, 2014.

P. Massoudifar, A. Rangarajan, A. Zare, and P. Gader, “An Integrated Graph Cuts Segmentation and Piece-wise Convex Unmixing Approach for Hyperspectral Imaging,” in 6th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014.

Y. Wang, Y. Li, K. C. Ho, A. Zare, and M. Skubic, “Sparsity Promoted Non-negative Matrix Factorization for Source Separation and Detection,” in 19th Int. Conf. Digital Signal Proc. (DSP), 2014, pp. 640-645.

A. Zare and D. T. Anderson, “Earth Movers Distance-based Simultaneous Comparison of Hyperspectral Endmembers and Proportions,” IEEE J. Sel. Topics. Appl. Earth Observ., vol. 7, pp. 1910-1921, June 2014.

A. Zare and K. C. Ho, “Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing,” IEEE Signal Process. Mag., vol. 31, pp. 95-104, Jan. 2014.

A. Zare and C. Jiao, “Extended Functions of Multiple Instances for Target Characterization,” in 6th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014.

 

2013

D. T. Anderson, A. Zare, and S. Price, “Comparing Fuzzy, Probabilistic, and Possibilistic Partitions Using the Earth Mover’s Distance,” IEEE Trans. Fuzzy Syst., vol. 21, pp. 766-775, Aug. 2013.

J. T. Cobb and A. Zare, “Multi-Image Texton Selection for Sonar Image Seabed Co-segmentation,” in Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 2013.

D. Dranishnikov, P. Gader, A. Zare, and T. Glenn, “Unmixing Using a Combined Microscopic and Macroscopic Mixture Model with Distinct Endmembers,” in 21st European Signal Proc. Conf. (EUSPICO), 2013.

X. Du, “Accounting for Spectral Variability in Hyperspectral Unmixing Using Beta Endmember Distribution,” Master Thesis, Columbia, MO, 2013.

J. Dula, A. Zare, D. Ho, and P. Gader, “Landmine Classification Using Possibilistic K-nearest Neighbors with Wideband Electromagnetic Induction Data,” in Proc. SPIE 8709 Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 2013.

T. Glenn, D. Dranishnikov, P. Gader, and A. Zare, “Subpixel Target Detection in Hyperspectral Imagery Using Piece-wise Convex Spatial-spectral Unmixing, Possibilistic and Fuzzy Clustering, and Co-registered LiDAR,” in IEEE Int. Geosci. and Remote Sensing Symp.(IGARSS)), Melbourne, VIC, 2013, pp. 1063-1066.

P. Khopkar and A. Zare, “Simultaneous Band-weighting and Spectral Unmixing for Multiple Endmember Sets,” in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS), Melbourne, VIC, 2013, pp. 2164-2167.

H. Kwon, X. Hu, J. Theiler, A. Zare, and P. Gurram, “Editorial: Algorithms for Multispectral and Hyperspectral Image Analysis,” J. Electr. Computer Eng., vol. 2013, 2013.

M. Popescu, J. Keller, and A. Zare, “A Framework for Computing Crowd Emotions Using Agent Based Modeling,” in IEEE Symp. Computational Intelli. Creativity and Affective Computing (CICAC), Singapore, 2013, pp. 25-31.

A. Zare, P. Gader, D. Dranishnikov, and T. Glenn, “Spectral Unmixing Using the Beta Compositional Model,” in 5th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013.

A. Zare, P. Gader, O. Bchir, and H. Frigui, “Piecewise Convex Multiple-model Endmember Detection and Spectral Unmixing,” IEEE Trans. Geosci. Remote Sens., vol. 51, pp. 2853-2862, May 2013.

A. Zare, P. Gader, and G. Casella, “Sampling Piecewise Convex Unmixing and Endmember Extraction,” IEEE Trans. Geosci. Remote Sens., vol. 51, pp. 1655-1665, Mar. 2013.

A. Zare and J. T. Cobb, “Sand Ripple Characterization Using an Extended Synthetic Aperture Sonar Model and MCMC Sampling Methods,” in IEEE OCEANS – San Diego, CA, 2013, pp. 1-7.

2012

D. T. Anderson and A. Zare, “Spectral Unmixing Cluster Validity Index for Multiple Sets of Endmembers,” IEEE J. Sel. Topics. Appl. Earth Observ., vol. 5, pp. 1282-1295, Aug. 2012.

R. Close, P. Gader, A. Zare, J. Wilson, and D. Dranishnikov, “Endmember Extraction Using the Physics-based Multi-mixture Pixel Model,” in Proc. SPIE 8515, Imaging Spectrometry XVII, 2012.

R. Close, P. Gader, J. Wilson, and A. Zare, “Using Physics-based Macroscopic and Microscopic Mixture Models for Hyperspectral Pixel Unmixing,” in Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 2012.

J. Dula, “Landmine Classification Using Possibilistic K-nearest Neighbors with Wideband Electromagnetic Induction Data,” Master Thesis, Columbia, MO, 2012.

P. Gader, D. Dranishnikov, A. Zare, and J. Chanussot, “A Sparsity Promoting Bilinear Unmixing Model,” in 4th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012.

N. Longbotham, F. Pacifici, T. Glenn, A. Zare, M. Volpi, D. Tuia, E. Christophe, J. Michel, J. Inglada, J. Chanussot, and Q. Du, “Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 Data Fusion Contest,” IEEE J. Sel. Topics Appl. Earth Observ., vol. 5, pp. 331-342, Feb. 2012.

A. Zare, Z. Fields, J. Keller, and J. Horton, “Agent-based Rumor Spreading Models for Human Geography Applications,” in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS), Munich, 2012, pp. 5394-5397.

A. Zare, P. Gader, T. Allgire, D. Drashnikov, and R. Close, “Bootstrapping for Piece-wise Convex Endmember Distribution Detection,” in 4th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012.

A. Zare, P. Gader, and K. S. Gurumoorthy, “Directly Measuring Material Proportions Using Hyperspectral Compressive Sensing,” IEEE Geosci. Remote Sens. Lett., vol. 9, pp. 323-327, May 2012.

A. Zare, O. Bchir, H. Frigui, and P. Gader, “Hyperspectral Image Analysis with Piece-wise Convex Endmember Estimation and Spectral Unmixing,” in 19th IEEE Int. Conf. Image Proc. (ICIP), Orlando, FL, 2012, pp. 2681-2684.

2011

A. Zare and P. Gader, “Multiclass Subpixel Target Detection Using Functions of Multiple Instances,” in Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 2011.

A. Zare and P. Gader, “Piece-wise Convex Spatial-spectral Unmixing of Hyperspectral Imagery Using Possibilistic and Fuzzy Clustering,” in IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE), Taipei, 2011, pp. 741-746.

A. Zare, M. Weiss, and P. Gader, “Rebuilding the Injured Brain: Use of MRS in Clinical Regenerative Medicine,” in Proc. SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2011.

A. Zare, “Spatial-spectral Unmixing Using Fuzzy Local Information,” in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS), Vancouver, BC, 2011, pp. 1139-1142.

A. Zare, P. Gader, J. Bolton, S. Yuksel, T. Dubroca, R. Close, and R. Hummel, “Sub-pixel Target Spectra Estimation and Detection Using Functions of Multiple Instances,” in 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011.

2010

O. Bchir, H. Frigui, A. Zare, and P. Gader, “Multiple Model Endmember Detection Based on Spectral and Spatial Information,” in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010.

A. Zare, O. Bchir, H. Frigui, and P. Gader, “A Comparison of Deterministic and Probabilistic Approaches to Endmember Representation,” in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010.

A. Zare and P. Gader, “An Investigation of Likelihoods and Priors for Bayesian Endmember Estimation,” in Proc. MaxEnt 2010: 30th Int. Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 2010, pp. 311-318.

A. Zare and P. Gader, “L1-endmembers: A Robust Endmember Detection and Spectral Unmixing Algorithm,” in Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 2010.

A. Zare and P. Gader, “Pattern Recognition Using Functions of Multiple Instances,” in 20th Int. Conf. Pattern Recognition (ICPR), 2010, pp. 1092-1095.

A. Zare and P. Gader, “PCE: Piecewise Convex Endmember Detection,” IEEE Trans. Geosci. Remote Sens., vol. 48, pp. 2620-2632, June 2010.

A. Zare, M. Silvious, R. Close, and P. Gader, “Quantifying the Benefit of Airborne and Ground Sensor Fusion for Target Detection,” in Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 2010.

A. Zare and P. Gader, “Robust Endmember Detection Using L1 Norm Factorization,” in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS), Honolulu, HI, 2010, pp. 971-974.

A. Zare, O. Bchir, H. Frigui, and P. Gader, “Spatially-smooth Piece-wise Convex Endmember Detection,” in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010.

2009

A. Zare and P. Gader, “Context-based Endmember Detection for Hyperspectral Imagery,” in 1st IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2009.

2008

A. Zare and P. Gader, “Endmember Detection Using the Dirichlet Process,” in 19th Int. Conf. Pattern Recognition (ICPR), 2008.

A. Zare and P. Gader, “Hyperspectral Band Selection and Endmember Detection Using Sparsity Promoting Priors,” IEEE Geosci. Remote Sens. Lett., vol. 5, pp. 256-260, Apr. 2008.

A. Zare, “Hyperspectral endmember detection and band selection using bayesian methods,” PhD Thesis, Gainesville, FL, 2008.

A. Zare, J. Bolton, P. Gader, and M. Schatten, “Vegetation Mapping for Landmine Detection Using Long-wave Hyperspectral Imagery,” IEEE Trans. Geosci. Remote Sens., vol. 46, pp. 172-178, Jan. 2008.

2007

A. Zare and P. Gader, “Sparsity Promoting Iterated Constrained Endmember Detection with Integrated Band Selection,” in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS), Barcelona, 2007, pp. 4045-4048.

A. Zare and P. Gader, “Sparsity Promoting Iterated Constrained Endmember Detection in Hyperspectral Imagery,” IEEE Geosci. Remote Sens. Lett., vol. 4, pp. 446-450, July 2007.

A. Zare and P. Gader, “SPICE: A Sparsity Promoting Iterated Constrained Endmember Extraction Algorithm with Applications to Landmine Detection from Hyperspectral Imagery,” in Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 2007.

2006

M. Schatten, P. Gader, J. Bolton, A. Zare, and A. Mendez-Vasquez, “Sensor Fusion for Airborne Landmine Detection,” in Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 2006.

2004

P. Gader, A. Mendez-Vasquez, K. Chamberlin, J. Bolton, and A. Zare, “Multi-sensor and Algorithm Fusion with the Choquet Integral: Applications to Landmine Detection,” in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS), Anchorage, AK, 2004, pp. 1605-1608 vol.3.

Preprint

S. J. Chang, R. Chowdhry, Y. Song, T. Mejia, A. Hampton, S. Kucharski, T. M. Sazzad, Y. Zhang, S. J. Koppal, C. H. Wilson, S. Gerber, B. Tillman, M. F. R. Resende, W. M. Hammond, A. Zare “HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study,” doi: https://doi.org/10.1101/2023.09.29.559614, 2023.

A. Seethepalli, L. York, H. Almtarfi, F. Fritschi and A. Zare, “A Novel Multi-perspective Imaging Platform (M-PIP) for Phenotyping Soybean Root Crowns in the Field Increases Throughput and Separation Ability of Genotype Root Properties,” in bioRxiv, 309161. 

R. Fick, P. Gader and A. Zare, “Robust Semi-Supervised Classification using GANs with Self-Organizing Maps”