Manifold learning methods have widely used in ordinary image processing domain. It has many advantages, depending on the different formulation of the manifold. Hyperspectral images are kind of images acquired by air-borne or space-born platforms. This paper introduces a novel manifold learning based dimension reduction (DR) method for hyperspectral classification. The purpose is to fully utiliz...

Spectral unmixing pursues the identification of spectrally pure constituents, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Most unmixing techniques have focused on the exploitation of spectral information alone. Recently, some techniques have been developed to take advantage of the complementary information provided by the spatial correlation of ...

This paper examines several hyperspectral data processing algorithms designed for a distributed computing environment. Due to the large size, hyperspectral data requires long computational times to process. In a distributed environment, the processing can be split into several components, many of them being executed simultaneously, thus leading to increased time efficiency. The algorithms are d...

Recently, real-time image data processing is a popular research area for hyperspectral remote sensing. In particular, target detection surveillance, which is an important military application of hyperspectral remote sensing, demands real-time or near real-time processing. The massive amount of hyperspectral image data seriously limits the processing speed. In this article, a strategy named spat...

The assignment of a quantitative spectral image quality metric is discussed with an approach proposed. Quality is divided into fidelity and utility components, with utility studied in the context of object detection in hyperspectral imagery. ©2010 Optical Society of America OCIS codes: (110.3000) Image quality assessment; (110.4234) Multispectral and hyperspectral imaging; (280.4788) Optical se...

In this paper, we describe a modification of the previously developed on-board image processing method applied to hyperspectral images. Algorithms on which the method is based were finalized and parametrically adjusted. Computational experiments consider formation and storage specifics for hyperspectral images. It has been shown that the proposed method based on HGIcompression can be recommende...

Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of thr...

The classification process support algorithms of shooting hyperspectral data, realizing objects’ identification of the Earth’s surface by means of their hyperspectral features’ analysis, received from the processed space images with application of various similarity measures, are considered. Identification algorithms on the base of Euclidean distance similarity measure, angular similarity measu...

Copyright © 2013 Heesung Kwon et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recent advances in multispectral and hyperspectral sensing technologies coupled with rapid growth in computing power have led to new opportunities...

Hyperspectral image classification has become an important research topic in remote sensing. Because of high dimensional data, a special attention is needed dealing with spectral data; and thus, one of the research topics in hyperspectral image classification is dimension reduction. In this paper, a dimension reduction approach is presented for classification on hyperspectral images. Advantages...