This paper describes a new algorithm for feature extraction in hyperspectral images based on Independent Component Analysis (ICA). The improvement introduced aims at reducing the computation times without decreasing the accuracy. Instead of using the entire image, we perform ICA processing on a subset of representative pixel vectors obtained through spectral screening. Spectral screening is a t...

With the number of channels in the hundreds instead of in the tens hyperspectral imagery possesses much richer spectral information than multispectral imagery .The increased dimensionality of such hyperspectral data provides a challenge to the current technique for analyzing data. Conventional classification methods may not be used without dimension reduction preprocessing. Supervised classific...

Analysis of hyperspectral data for defining the land-cover classes through classification techniques, in particular for small patches and scattered land-covers, is not a trivial task. Factors such as high spatial variability of landcover signatures, the “boundary effect” between neighboring land-covers, and the curse of dimensionality make this task more challenging [1]. As the integrity of a l...

This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is t...

Fukunaga-Koontz Transform (FKT) is a statistical technique which has many application areas for two-class classification or detection problems. In this paper, we have proposed improved target detection algorithm for hyperspectral imagery (HSI) based on enhanced FKT which gives better results for multi-class target detection problems. Hyperspectral imagery is popular for target detection applica...

Mathematical morphology is a non-linear technique for spatial image analysis that has found many applications in different areas. This chapter reports on the extension of morphological image processing to hyperspectral imagery. In order to define extended morphological operations, a physically meaningful distance-based vector organization scheme is introduced, and fundamental vector operations ...

With the advent of hyperspectral imaging spectrometers comes the need for procedures that detect and interrogate spectral quantities of interest. Such procedures or algorithms play a key role in the dissemination and interpretation of hyperspectral data. Validation of these algorithms involves well-characterized field collection campaigns that can be time and cost prohibitive. Radiometrically, ...

Automatic target recognition (ATR) in hyperspectral imagery is a challenging problem due to recent advances of remote sensing instruments which have significantly improved sensor’s spectral resolution. As a result, small and subtle targets can be uncovered and extracted from image scenes, which may not be identified by prior knowledge. In particular, when target size is smaller than pixel resol...

This paper is an evaluation of a previously proposed noise reduction technology for hyperspectral imagery to examine whether it could help to better serve remote sensing applications after noise reduction using the technology. Target detection from hyperspectral imagery is selected as an example for the evaluation. A hyperspectral datacube acquired using the airborne Short-wave-infrared Full Sp...

Coverage and frequency of remotely sensed forest structural information would benefit from single orbital platforms designed to collect sufficient data. We evaluated forest structural information content using single-date Hyperion hyperspectral imagery collected over full-canopy oak-hickory forests in the Ozark National Forest, Arkansas, USA. Hyperion spectral derivatives were used to develop m...