The purpose of this study is to validate the accuracy of abundance map reference data (AMRD) for three airborne imaging spectrometer (IS) scenes. AMRD refers to reference data maps (“ground truth”) that are specifically designed to quantitatively assess the performance of spectral unmixing algorithms. While classification algorithms typically label whole pixels as belonging to certain ground co...

Over the past years, many algorithms have been developed for multispectral and hyperspectral image classification. A general approach to mixed pixel classification is linear spectral unmixing, which uses a linear mixture model to estimate the abundance fractions of signatures within a mixed pixel. As a result, the images generated for classification are usually gray scale images, where the gray...

This paper details various aspects of the detection and identification of chemical plumes in long wave infrared (LWIR) data. The lack of well defined edges and the dynamic nature of a gas cloud leads to challenges in detection, particularly when the cloud diffuses and becomes thin. Contemporary graph segmentation algorithms are investigated to track the movement of the gaseous cloud as it sprea...

Hyperspectral imaging can be used in assessing the quality of foods by decomposing the image into constituents such as protein, starch, and water. Observed data can be considered a mixture of underlying characteristic spectra (endmembers), and estimating the constituents and their abundances requires efficient algorithms for spectral unmixing. We present a Bayesian spectral unmixing algorithm e...

Onboard compression of hyperspectral imagery is important for reducing the burden on downlink resources. Here we describe a novel adaptive predictive technique for lossless compression of hyperspectral data. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that is competitive with the best results from the literature. Al...

Mixed pixels, which are inevitable in remote sensing images, often result in a lot of limitations in their applications. A novel approach for mixed pixel’s fully constrained unmixing, Fully Constrained Oblique Subspace Projection (FCOBSP) Linear Unmixing algorithm, is proposed to handle this problem. The Oblique Subspace Projection, in which the signal space is oblique to the background space, ...

This paper details various aspects of the detection and identification of chemical plumes in long wave infrared (LWIR) data. The lack of well defined edges and the dynamic nature of a gas cloud leads to challenges in detection, particularly when the cloud diffuses and becomes thin. Contemporary graph segmentation algorithms are investigated to track the movement of the gaseous cloud as it sprea...

Hyperspectral Imagery Sensing (HIS) is widely gained tremendous popularity in many research areas such as remotely sensed satellite imaging and aerial reconnaissance. HIS is an important technique with the measurement, analysis, and interpretation of spectra acquired sensing scene an airborne or satellite sensor. The development of sensor technology brought the developing of collecting image da...

This paper is a personal and purposefully idiosyncratic survey of issues, some technical and some philosophical, that arise in developing algorithms for the detection of anomalies and anomalous changes in hyperspectral imagery. The technical emphasis in anomaly detection is on modeling background clutter. For hyperspectral imagery this is a challenge because there are so many channels (the hype...