With success of Deep Belief Networks (DBNs) in computer vision, DBN has attracted great attention in hyperspectral classification. Many deep learning based algorithms have been focused on deep feature extraction for classification improvement. Multi-features, such as texture feature, are widely utilized in classification process to enhance classification accuracy greatly. In this paper, a novel...

Hyperspectral image contains fine spectral and spatial resolutions for generating accurate land use and land cover maps. Supervised classification is the one of method used to exploit the information from the hyperspectral image. The traditional supervised classification methods could not be able to overcome the limitations of the hyperspectral image. The multiple classifier system (MCS) has th...

Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spec...

Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from the corresponding hyperspectral signatures containing informati...

Hyperspectral images, contain data from a large number of contiguous bands and, therefore, cannot be displayed directly using a color display system. In this paper, an independent component analysis-based (ICA-based) approach for the problem of fusing hyperspectral images to three-band images for color display purposes is proposed. Correlation coefficient and mutual information (ICA-CCMI) are u...

Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spec...

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)...