Hyperspectral image processing has been a very dynamic area in remote sensing and other applications in recent years. Hyperspectral images provide ample spectral information to identify and distinguish spectrally similar materials for more accurate and detailed information extraction. Wide range of advanced classification techniques are available based on spectral information and spatial inform...

Developing computationally efficient processing techniques for massive volumes of hyperspectral data is critical for space-based Earth science and planetary exploration (see for example, (Plaza & Chang, 2008), (Henderson & Lewis, 1998) and the references therein). With the availability of remotely sensed data from different sensors of various platforms with a wide range of spatiotemporal, radio...

anomaly detection (ad) has recently become an important application of target detection in hyperspectral images. the reed-xialoi (rx) is the most widely used ad algorithm that suffers from “small sample size” problem. the best solution for this problem is to use dimensionality reduction (dr) techniques as a pre-processing step for rx detector. using this method not only improves the detection p...

A LMOST A DECADE after the milestone special issue of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) dedicated to the analysis of hyperspectral image data, edited by Prof. Landgrebe, Prof. Serpico, Prof. Crawford, and Prof. Singhroy [1], it is a great pleasure to introduce this new special issue on hyperspectral image and signal processing. In the intervening years, interest in h...

This paper presents processes and performance for ground-based measurements used by The Boeing Company and RESOURCE21 for vicarious calibration tests of airborne and satellite multispectral and hyperspectral sensors. We highlight our methodology for measuring downwelling irradiance and scene radiance and reflectance from 400 to 2500 nm, and describe in-house enhancements made to commercial spec...

The advent of hyperspectral image technology is a major leap in recent years, it obtains the surface of the earth image contains rich space, radiation and spectral information, Mixed pixels not only effects identification and classification precision of object, but also greatly hinder the development of quantitative remote sensing, so effectively interpret mixed pixels is an important problem f...

Feature selection is one of the fundamental problems in nearly every application of statistical modeling, and hyperspectral data analysis is no exception. We propose a new methodology for combining unsupervised and supervised methods under classification accuracy and computational requirement constraints. It is designed to perform not only hyperspectral band (wavelength range) selection but als...

In this work we propose a hierarchical clustering methodology for hyperspectral data based on the Hotelling’s T 2 statistic. For each hypespectral sample data, the statistical sample mean is calculated using a window-based neighborhood. Then, the pairwise similarities between any two hyperspectral samples are computed in base to the Hotelling’s T 2 statistic. This statistic assumes a Gaussian d...

Hyperspectral sensors acquire images with hundreds of spectral channels. These images have a lot of information in both spectral and spatial domain, and with this kind of information different research studies can be accomplished. In this work, we present several optimizations for hyperspectral image processing algorithms intended to detect targets in hyperspectral images. The hyperspectral ima...

Hyperspectral anomaly detection is one of the main challenging topics in both military and civilian fields. The spectral information contained in a hyperspectral cube provides a high ability for anomaly detection. In addition, the costly spatial information of adjacent pixels such as texture can also improve the discrimination between anomalous targets and background. Most studies miss the wort...