Image noise is generated unavoidably in the hyperspectral image acquision process and has a negative effect on subsequent image analysis. Therefore, it is necessary to perform image denoising for hyperspectral images. This paper proposes a cubic total variation (CTV) model by combining the 2-D total variation model for spatial domain with the 1-D total variation model for spectral domain, and t...

Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower th...

In this paper we provide a brief review of recent advances in computational methods for hyperspectral image analysis with emphasis in hybrid approaches. Hyperspectral imagery acquisition and hyperspectral analysis are growing elds. The analysis of hyperspectral images will have an increasing impact in several application areas, i.e., Earth observation, planetology, food industry, quality proces...

the purpose of this study is the fusion of polarimetric sar and hyperspectral images for concealed target detection in forest areas. the proposed method is using the polarimetric and the spectral signatures. a radarsat-2 image and a hyperion image of the city san francisco were chosen for evaluation of the proposed method. the four targets were selected in the forest and the outer areas. three ...

Because of the great data of the hyperspectral image, it is not good for storage and transmission. To work out a high efficiency compression method for hyperspectral image is essential. Prediction encoding is easy to realize and has been studied widely in the hyperspectral image compression field. High compression ratio, resolution independence and fast decoding speed are the main advantages of...

Hyperspectral image compression has become increasingly important in data exploitation because of enormous data volumes and high redundancy provided by hundreds of contiguous spectral channels. Since a hyperspectral image can be viewed as a 3-dimensional (3D) image cube, many efforts have been devoted to extending 2D image compression techniques to perform 3D image compression on hyperspectral ...

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Hyperspectral Imagery Compression: State of the Art. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Outline of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....

Hyperspectral imaging, also known as imaging spectroscopy, captures a data cube of a scene in two spatial and one spectral dimension. Hyperspectral image analysis refers to the operations which lead to quantitative and qualitative characterization of a hyperspectral image. This thesis contributes to hyperspectral imaging and analysis methods at multiple levels. In a tunable filter based hypersp...

Existing hyperspectral imaging systems produce low spatial resolution images due to hardware constraints. We propose a sparse representation based approach for hyperspectral image super-resolution. The proposed approach first extracts distinct reflectance spectra of the scene from the available hyperspectral image. Then, the signal sparsity, non-negativity and the spatial structure in the scene...