The Kernel-RX detector (KRXD) has attracted widespread interest in hyperspectral image processing with the utilization of nonlinear information. However, the kernelization of hyperspectral data leads to poor execution efficiency in KRXD. This paper presents an approach to the progressive line processing of KRXD (PLP-KRXD) that can perform KRXD line by line (the main data acquisition pattern). P...

Digital image processing is collection of techniques for the manipulation of digital images by computer and its applications. This collection of methods in remote sensing is dominantly treated as Satellite Digital Image Processing (SDIP). A spaceborne Multispectral Image Processing System (MIPS) has been used since 1960 as a traditional satellite image processing system for data analysis and ex...

Spectral unmixing is focused in the identification of spectrally pure signatures, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Mainly focused on the spectral information contained in the hyperspectral images, endmember extraction techniques have recently included spatial information to achieve more accurate results. Several algorithms have been d...

Classification of heterogeneous classes present in the Hyperspectral image is one of the recent research issues in the field of remote sensing. This work presents a novel technique that classifies Hyperspectral images that contain number of classes by making use of the image moments. Recently, researchers have introduced a number of neural network models and structured output based methods for ...

This paper presents an original research for hyperspectral satellite image compression using a fully neural system with the following processing stages: (1) a Hebbian network performing the principal component selection; (2) a system of "k" circular self-organizing maps for vector quantization of the previously extracted components. The software implementation of the above system has been train...

We have developed a new and innovative technique for combining a high-spatial-resolution multispectral image with a lower-spatial-resolution hyperspectral image. The approach, called CRISP, compares the spectral information present in the multispectral image to the spectral content in the hyperspectral image and derives a set of equations to approximately transform the multispectral image into ...

Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied by systems like the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS), developed by NASA Jet Propulsion Laboratory, allows their exploitation in diverse applications, such as detection and control of wildland fi...

The rapid development of space and computer technologies allows for the possibility to store huge amounts of remotely sensed image data, collected using airborne and satellite instruments. In particular, NASA is continuously gathering high-dimensional image data with Earth observing hyperspectral sensors such as the Jet Propulsion Laboratory’s airborne visible–infrared imaging spectrometer (AVI...

Hyperspectral Images are a set of datasets used in the recognition and mapping of surface materials such as minerals and vegetation affiliated to ore deposits. Usually these hyperspectral Image datasets are of high dimensionality which makes its classification process a complex task and of low accuracy using conventional classification approaches since we don’t have enough observations in estim...

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...