Hyperspectral data provide new capabilities for discriminating spectrally similar classes, but such class signatures sometimes will be difficult to analyze. To incorporate reliable useful information could help, but at the same time, may also lead increased dimensionality of the feature vector making the hyperspectral data larger than expected. It is challenging to apply discriminative informat...

The idea of using artificial neural network has proved useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier. To improve the performance of neural network classifier, wavelet-based feature extraction algorithms are applied to extract useful features for hyperspec...

Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gather...

A spectral linear prediction compression scheme for lossless compression of hyperspectral images is proposed in this paper. Since hyperspectral images have a great deal of correlation from band to band, spectral linear prediction algorithm, which utilizes information from several bands, is very efficient for compression purposes. The proposed algorithm is compared to JPEG-LS and CALIC encoding ...

Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using ...

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...

Hyperspectral data visualizations are useful as a background layer to labeling information in the hyperspectral scene such as classification information, locations, or geographic features. Given a hyperspectral image H , where the ith-jth pixel Hij is a d-dimensional vector representing reflectance at d wavelengths, any dimensionality-reduction method an be used to reduce the d dimensions down ...

Hyperspectral images (HSIs) has become very popular area of research. This paper deals with the compression and classification of Hyperspectral images using Discrete Wavelet Technique in conjunction with Non negative Tucker Decomposition. This algorithm exploits both the spectral and the spatial information of the images. The core idea behind the proposed technique is to apply TD on the DWT coe...

Remote sensing has become an important source of urban land-use/cover classification, and as a result of their high spatial and spectral resolution, airborne hyperspectral images have been widely used to distinguish different urban classes. However, the previous studies into the classification of urban environments have mainly focused on a supervised scenario, which is limited by the selection ...

Hyperspectral cameras preserve the fine spectral details of scenes that are generally lost in the traditional RGB cameras due to the gross quantization of radiance. These details are desirable in numerous imaging applications, nevertheless the high cost of hyperspectral hardware and the associated physical constraints currently limit the pervasive use of hyperspectral imaging. We take a computa...