The technology of hyperspectral remote sensing image improves the capability of collecting the objects such as lakes, rivers, farmlands, buildings, forest and desert in the ground surface. Since the spatial resolution is becoming higher recently, image segmentation of hyperspectral remote sensing is important to the next step of remote sensing image classification and object recognition. In thi...

Data simulation is widely used in remote sensing to produce imagery for a new sensor in the design stage, for scale issues of some special applications, or for testing of novel algorithms. Hyperspectral data could provide more abundant information than traditional multispectral data and thus greatly extend the range of remote sensing applications. Unfortunately, hyperspectral data are much more...

Hyperspectral imaging and analysis refers to the capture and understanding of image content in multiple spectral channels. Satellite and airborne hyperspectral imaging has been the focus of research in remote sensing applications since nearly the past three decades. Recent use of ground-based hyperspectral imaging has found immense interest in areas such as medical imaging, art and archaeology,...

Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications (e.g., mining and geology applications, ecology, surveillance, etc.). A hyperspectral image has a three-dimensional structure (a sort of datacube)...

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...