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 remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad. The research results of identification and classification of forest tree species, grassland and urban land features were summa...

An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with compressive sensing of large data, which is typical to hyperspectral imaging. The syste...

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...

BACKGROUND Segmentation of hyperspectral medical images is one of many image segmentation methods which require profiling. This profiling involves either the adjustment of existing, known image segmentation methods or a proposal of new dedicated methods of hyperspectral image segmentation. Taking into consideration the size of analysed data, the time of analysis is of major importance. Therefor...

A technique of spatial-spectral quantization of hyperspectral images is introduced. Thus a quantized hyperspectral image is just summarized by K spectra which represent the spatial and spectral structures of the image. The proposed technique is based on α−connected components on a region adjacency graph. The main ingredient is a dissimilarity metric. In order to choose the metric that best fit ...

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...

To enable industrial adoption of hyperspectral imaging we have developed a unique integrated hyperspectral filter/imager technology. The spectral filters are monolithically deposited/integrated on top of CMOS imagers at wafer level. The materials of the filters are chosen such that they are compatible with the production flows available in most CMOS foundries. The result is a compact & fast hyp...

Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight unmanned aerial vehicle (UAV). The snapshot camera is a relatively new type of hyperspe...