Oil spill is one of major marine environmental challenges. The main impacts of this phenomenon are preventing light transmission into the deep water and oxygen absorption, which can disturb the photosynthesis process of water plants. In this research, we utilize SpecTIR airborne sensor data to extract and classify oils spill for the Gulf of Mexico Deepwater Horizon (DWH) happened in 2010. For t...

In this paper we provide a brief review of recent advances in computational methods for hyperspectral image analysis with emphasis in hybrid approaches. Hyperspectral imagery acquisition and hyperspectral analysis are growing elds. The analysis of hyperspectral images will have an increasing impact in several application areas, i.e., Earth observation, planetology, food industry, quality proces...

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...

The quantitative evaluation of algorithms applied to remotely sensed hyperspectral imagery require data sets with known ground truth. A recent data collection known as SHARE 2012, conducted by scientists in the Digital Imaging and Remote Sensing Laboratory at the Rochester Institute of Technology together with several outside collaborators, acquired hyperspectral data with this goal in mind. Se...

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...

In this paper, we describe two fully Bayesian algorithms that have been previously proposed to unmix hyperspectral images. These algorithms relies on the widely admitted linear mixing model, i.e. each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra. First, the unmixing problem is addressed in a supervised framework, i.e., when the endmembers are ...

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...

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...

We present new algorithms that perform unmixing in hyperspectral images and then recognize targets whose spectral signatures are given. The target can occupy subor above pixel. These algorithms combine ideas from algebra and probability theory. Experimental results demonstrate the efficiency and the robustness of these algorithms on real hyperspectral data.

Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hundreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scanner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vectors (collection of signals acquire...