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...

Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of thr...

Hyperspectral imaging has proven significance in bio-imaging applications and it has the ability to capture up to several hundred images of different wavelengths offering relevant spectral signatures. To use hyperspectral imaging for in vivo monitoring and diagnosis of the internal body cavities, a snapshot hyperspectral video-endoscope is required. However, such reported systems provide only a...

Abstract: Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing. L1/2 and L2 regularizers can be added to NMF to enforce sparseness and evenness, respectively. In practice, a region in a hyperspectral image may posses...

This chapter focuses on the problem of recovering a hyperspectral image descriptor based upon harmonic analysis. It departs from the use of integral transforms to model hyperspectral images in terms of probability distributions. This provides a link between harmonic analysi and affine geometric transformations between object surface planes in the scene. Moreover, the use of harmonic analysis pe...

Multi-spectral and hyperspectral sensors have been deployed on airborne platforms for many years. Typical scenarios for airborne collection of spectral data involve over-flights of target areas followed by ground-based data calibration and exploitation. With the advent of flight-qualified, high-performance, parallel computing hardware, however, practical near real-time processing and exploitati...

This paper introduces a hyperspectral image classification approach by using pixel spatial relationship. In hyperspectral images, the spatial relationship among pixels has been shown to be important in the exploration of pixel labels. To better employ the spatial information, we propose to estimate the correlation among pixels in a hypergraph structure. In the constructed hypergraph, each pixel...

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized ...

—Hyperspectral imagery from EO-1 Hyperion and AVIRIS were used in conjunction with continuous forest inventory (CFI) data to map detailed forest composition in the state forests of Western Maryland. We developed a hierarchical vegetation classification that conformed to the National Vegetation Classification Standard (NVCS) at the Alliance level and mapped these forest types as a function of hy...

Hyperspectral imaging is a new technique in remote sensing that collects hundreds of images, at different wavelength values, for the same area in the surface of the Earth. For instance, the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) instrument operated by NASAs Jet Propulsion Laboratory collects 224 spectral channels in the wavelength range from 40 to 250 nanometers using narrow s...