This paper briefly describes the methods available for collection, atmospheric and geometric correction, and processing of hyperspectral imagery. Discussion of data capture concentrates on logistics of integrating image acquisition with field data collection. Atmospheric correction is required to use the imagery with reference spectra from field and laboratory sensors; a variety of methods for ...

An investigation of the estimation of leaf biochemistry in open tree crop canopies using high-spatial hyperspectral remote sensing imagery is presented. Hyperspectral optical indices related to leaf chlorophyll content were used to test different radiative transfer modelling assumptions in open canopies where crown, soil and shadow components were separately targeted using 1 m spatial resolutio...

Category decomposition method based on matched filter for un-mixing of mixed pixels: mixels which are acquired with spaceborne based hyperspectral radiometers is proposed. Through simulation studies with simulated mixed pixels which are created with spectral reflectance data derived from USGS spectral library as well as actual airborne based hyperspectral radiometer imagery data, it is found th...

Hyperspectral sensors are passive sensors that simultaneously record images for hundreds of contiguous and narrowly spaced regions of the electromagnetic spectrum. Each image corresponds to the same ground scene, thus creating a cube of images that contain both spatial and spectral information about the objects and backgrounds in the scene. In this paper, we present an adaptive anomaly detector...

The assignment of a quantitative spectral image quality metric is discussed with an approach proposed. Quality is divided into fidelity and utility components, with utility studied in the context of object detection in hyperspectral imagery. ©2010 Optical Society of America OCIS codes: (110.3000) Image quality assessment; (110.4234) Multispectral and hyperspectral imaging; (280.4788) Optical se...

Early detection of insect or pathogen infestations in forests would be useful to forest managers who want to make decisions that minimize timber losses. Typical methods of forest reconnaissance to detect infestations have included analysis of multispectral imagery. Multispectral imagery, however, often lacks the sensitivity to detect subtle changes in tree canopy reflectance because of physiolo...

There has been significant research on pan-sharpening multispectral imagery with a high resolution image, but there has been little work extending the procedure to high dimensional hyperspectral imagery. We present a wavelet-based variational method for fusing a high resolution image and a hyperspectral image with an arbitrary number of bands. To ensure that the fused image can be used for task...

Analysis of multi/hyperspectral imagery necessitates a selection of an optimal subset of bands in order to avoid the Hughes phenomena and parameter estimation problems due to interband correlation. This can be achieved by employing feature extraction techniques for significant reduction of data dimensionality. From the perspective of statistical pattern recognition, feature extraction refers to...

This paper is a personal and purposefully idiosyncratic survey of issues, some technical and some philosophical, that arise in developing algorithms for the detection of anomalies and anomalous changes in hyperspectral imagery. The technical emphasis in anomaly detection is on modeling background clutter. For hyperspectral imagery this is a challenge because there are so many channels (the hype...

The quality of remotely sensed hyperspectral images is not easily assessed visually, as the value of the imagery is primarily inherent in the spectral information embedded in the data. In the context of earth observation or defense applications, hyperspectral images are generally defined as high spatial resolution (1 to 30 meter pixels) imagery collected in dozens to hundreds of contiguous narr...