Hyperspectral images can be represented either as a set of images or as a set of spectra. Spectral classification and segmentation and data reduction are the main problems in hyperspectral image analysis. In this paper we propose a Bayesian estimation approach with an appropriate hiearchical model with hidden markovian variables which gives the possibility to jointly do data reduction, spectral...

Hyperspectral imaging comprises the technologies that incorporates remote sensing and analysis of an object or specific area of the earth at different distances with very large number of bands. Currently, a wide range of hyperspectral data sets are obtained continuously, in addition to conventional multispectral remote sensing images, and presented to users by institutions for both commercial a...

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...

Hyperspectral images have been used in anomaly and change detection applications such as search and rescue operations where it is critical to have fast detection. However, conventional Reed-Xiaoli (RX) algorithm [6] took about 600 seconds using a PC to finish the processing of an 800x1024 hyperspectral image with 10 bands. This is not acceptable for real-time applications. A more recent algorit...

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...

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...

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 reviews the recently developed processing techniques for remotely sensed imagery, including very high resolution (VHR) information extraction, super resolution techniques, hyperspectral image processing and object detection, and also some artificial intelligence approaches.

Parametric geocoding is a standard procedure for orthorectification of hyperspectral airborne scanner data. Boresight parameters are estimated by using the image coordinates of precisely known ground reference points. To fully automate the processing of the recorded hyperspectral data ground reference points should be selected and matched by an algorithm without user interaction. In this articl...

Hyperspectral imagery is nowaday widely used in numerous image processing fields. This imagery technique simultaneously acquires up to several hundreds of images of a same scene at different spectral wavelengths and stack them all in a data cube. Each pixel is therefore no longer a triplet of values as it is the case in classical RGB imagery, but a n−dimensional vector corresponding to a reflec...