Abstract: While the Choquet integral (ChI) is a powerful parametric nonlinear aggregation function, it has limited scope and is not a universal function generator. Herein, we focus on a class of problems that are outside the scope of a single… Read More
PublicationPublication
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
Partial Membership Latent Dirichlet Allocation for Soft Image Segmentation
![](https://faculty.eng.ufl.edu/machine-learning/wp-content/uploads/sites/58/2016/09/sun-1-e1473638758156-150x150.jpg)
Abstract: Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there… Read More
Multi-camera High-throughput Plant Root Phenotyping System
Abstract: Plant root phenotyping is a key component in plant breeding and selection for desireable root properties. Preferable root traits can not only help a plant to grow faster but also allow for more dense and deep root system architectures… Read More
Map-guided Hyperspectral Image Superpixel Segmentation Using Semi-supervised Partial Membership Latent Dirichlet Allocation
Abstract: Many superpixel segmentation algorithms which are suitable for the regular color images like images with three channels: red, green and blue (RGB images) have been developed in the literature. However, because of the high dimensionality of hyperspectral imagery, these… Read More
Semi-supervised Interactive Unmixing for Hyperspectral Image Analysis
Abstract: In the past several decades, hyperspectral imaging has drawn a lot of attention in the eld of remote sensing. Yet, due to low spatial resolutions of hyperspectral imagers, often the response from more than one surface material can be… Read More
Random Projection below the JL Limit
Abstract: The Johnson-Lindenstrauss (JL) lemma, with known probability, sets a lower bound q0 on the dimension for which a random projection of p-dimensional vector data is guaranteed to be within (1±ε) of being an isometry in a randomly projected downspace.… 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
Discriminative Multiple Instance Hyperspectral Target Characterization
![](https://faculty.eng.ufl.edu/machine-learning/wp-content/uploads/sites/58/2016/09/MIACE-150x150.jpg)
Abstract: In this paper, two methods for multiple instance target characterization, MI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative target signature from imprecisely-labeled and mixed training data. In many applications, such as sub-pixel target detection in remotely-sensed… Read More