Algorithms for a rapid analysis of hyperspectral data are becoming more and more important with planned next generation spaceborne hyperspectral missions such as the Environmental Mapping and Analysis Program (EnMAP) and the Japanese Hyperspectral Imager Suite (HISUI), together with an ever growing pool of hyperspectral airborne data. The here presented EnGeoMAP 2.0 algorithm is an automated sy...

Person re-identification in a multi-camera environment is an important part of modern surveillance systems. Person reidentification from color images has been the focus of much active research, due to the numerous challenges posed with such analysis tasks, such as variations in illumination, pose and viewpoints. In this paper, we suggest that hyperspectral imagery has the potential to provide u...

This study explored a combination of hyperspectral and lidar systems for vegetation mapping in the Florida Everglades. A framework was designed to integrate two remotely sensed datasets and four data processing techniques. Lidar elevation and intensity features were extracted from the original point cloud data to avoid the errors and uncertainties in the raster-based lidar methods. Lidar signif...

Hyperspectral sounder data is used for retrieval of surface properties and atmospheric temperature, moisture, trace gases, clouds and aerosols. This large volume three-dimensional data is taken from many scan lines containing cross-track footprints, each with thousands of infrared channels. Unlike hyperspectral imager data compression, hyperspectral sounder data compression is better to be loss...

Hyperspectral images can be represented either as a set of images or as a set of spectra. Spectral classification and segmentation and data reduction are the main problems in hyperspectral image analysis. In this paper we propose a Bayesian estimation approach with an appropriate hiearchical model with hidden markovian variables which gives the possibility to jointly do data reduction, spectral...

Hyperspectral imaging comprises the technologies that incorporates remote sensing and analysis of an object or specific area of the earth at different distances with very large number of bands. Currently, a wide range of hyperspectral data sets are obtained continuously, in addition to conventional multispectral remote sensing images, and presented to users by institutions for both commercial a...

Intra-operative measurements of tissue shape and multi/ hyperspectral information have the potential to provide surgical guidance and decision making support. We report an optical probe based system to combine sparse hyperspectral measurements and spectrally-encoded structured lighting (SL) for surface measurements. The system provides informative signals for navigation with a surgical interfac...

In this paper, we propose a novel contextual classification of hyperspectral data. We use probabilistic label relaxation (PLR) process to incorporate context information into the spectral pixelwise classification procedure. In conventional PLR procedure, first a maximum likelihood classification is performed and class probabilities are computed by using multivariate normal models. However this ...

This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation, with two main steps: (a) learning, for each class label, the posterior probability distributions, based on a multinomial logistic regression model; (b) segmenting the hyperspectral image, based on the posterior probability distribution learnt in step (a) and on a multi-level logistic prior encoding the...

The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing. The high dimensionality of hyperspectral data, presence of substantial noise, and overlap of classes all contribute to the difficulty of automatically segmenting and clustering hyperspectral images. In this article, we propose an unsupervised learning technique that combin...