Onboard compression of hyperspectral imagery is important for reducing the burden on downlink resources. Here we describe a novel adaptive predictive technique for lossless compression of hyperspectral data. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that is competitive with the best results from the literature. Al...

Hyperspectral Imagery Sensing (HIS) is widely gained tremendous popularity in many research areas such as remotely sensed satellite imaging and aerial reconnaissance. HIS is an important technique with the measurement, analysis, and interpretation of spectra acquired sensing scene an airborne or satellite sensor. The development of sensor technology brought the developing of collecting image da...

Anomaly detection is attractive for the analysis of hyperpectral imagery. This paper describes an expanded anomaly detection algorithm for small targets in hyperspectral imagery. As a variant of the well known multivariate anomaly detector called RX algorithm, the approach called the dimension reduction RX algorithm (DRRX) is proposed. The analytical fusion strategy is incorporated into the RX ...

Field test results are presented for a prototype long-wave adaptive imager that provides both hyperspectral imagery and contrast imagery based on the direct application of hyperspectral detection algorithms in hardware. Programmable spatial light modulators are used to provide both spectral and spatial resolution using a single element detector. Programmable spectral and spatial detection filte...

Hyperspectral imagery is nowaday widely used in numerous image processing fields. This imagery technique simultaneously acquires up to several hundreds of images of a same scene at different spectral wavelengths and stack them all in a data cube. Each pixel is therefore no longer a triplet of values as it is the case in classical RGB imagery, but a n−dimensional vector corresponding to a reflec...

High-resolution visible remote sensing imagery and thermal infrared hyperspectral imagery are potential data sources for land-cover classification. In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-cover classification method based on the fusion of visible and thermal infrared hyperspectral imagery is proposed, namely, SSECRF (spatial-spe...

—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 imagery with moderate spatial resolution (~30m) presents an interesting challenge to feature extraction algorithm developers, as both spatial and spectral signatures may be required to identify the feature of interest. We describe a genetic programming software system, called GENIE, which augments the human scientist/analyst by evolving customized spatiospectral feature extraction...

Motivation. Multi and Hyperspectral remote sensing imagery provide valuable insights regarding the composition of a scene and significantly facilitate tasks like object and material recognition, spectral unmixing and region clustering, among others [1], [2]. However, current remote sensing imaging architectures are unable to concurrently acquire high spatial and spectral resolution imagery, due...

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sen...