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
Tag: unmixing
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
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
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
Hyperspectral Unmixing With Endmember Variability via Alternating Angle Minimization
Abstract: In hyperspectral unmixing applications, one typically assumes that a single spectrum exists for every endmember. In many scenarios, this is not the case, and one requires a set or a distribution of spectra to represent an endmember or class.… Read More