Congratulations to our labmates Ron Fick and Susan Meerdink for being accepted to the 2019 IEEE WHISPERS conference in Amsterdam! The WHISPERS conference is an annual workshop focusing on advances in remote sensing with hyperspectral data. Ron will present on… Read More
Tag: endmember
Developing Spectral Libraries Using Multiple Target Multiple Instance Adaptive Cosine/Coherence Estimator
Abstract: Traditional methods of developing spectral libraries for unmixing hyperspectral images tend to require domain knowledge of the study area and the material’s spectra. In this paper, we propose using the Multiple Target Multiple Instance Adaptive Cosine/Coherence Estimator (Multi-Target MI-ACE)… Read More
Temporal Mapping of Hyperspectral Data
Abstract: The increasing popularity of hyperspectral sensors is dramatically increasing the temporal availability of data. To date, algorithms struggle to compare hyperspectral data collected across dates due to different environmental conditions during collection. In this work, we develop a temporal… Read More
Target Concept Learning From Ambiguously Labeled Data
Abstract: The multiple instance learning problem addresses the case where training data comes with label ambiguity, i.e., the learner has access only to inaccurately labeled data. For example, in target detection from remotely sensed hyperspectral imagery, targets are usually sub-pixel… Read More
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection
Abstract: The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented. In many hyperspectral target detection problems, acquiring accurately labeled training data is difficult. Furthermore, each pixel containing target is likely to be a… Read More
Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation
Abstract: A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability and spatial information. Partial Membership Latent Dirichlet Allocation is an effective approach for spectral unmixing while representing spectral… Read More
Classification Label Map for MUUFL Gulfport Released!
We are excited to announce that we have released a classification label map for the MUUFL Gulfport co-registered hyperspectral and Lidar Campus 1 image . The MUUFL Gulfport data set was collected in November 2010 over the campus of the… Read More
Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps
Abstract: A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral-appropriate version of the SLIC superpixel segmentation algorithm, leverages map information to guide segmentation, and incorporates the semi-supervised Partial Membership Latent Dirichlet… Read More
Multiple Instance Hybrid Estimator for Learning Target Signatures
Abstract: Signature-based detectors for hyperspectral target detection rely on knowing the specific target signature in advance. However, target signature are often difficult or impossible to obtain. Furthermore, common methods for obtaining target signatures, such as from laboratory measurements or manual… Read More
Hyperspectral Unmixing with Endmember Variability Using Partial Membership Latent Dirichlet Allocation
Abstract: The application of Partial Membership Latent Dirichlet Allocation (PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA provides a model for a hyperspectral image analysis that accounts for spectral variability and incorporates spatial information through the use… Read More