7 processing. Conventional dimensionality reduction on-board remote devices is 8 often prohibitive due to limited computational resources; on the other hand, 9 integrating random projections directly into signal acquisition offers an alternative 10 to explicit dimensionality reduction without incurring sender-side computational 11 cost. Receiver-side reconstruction of hyperspectral data from su...

Hyperspectral band image selection is a fundamental problem for hyperspectral remote sensing data processing. Accepting its importance, several information-based band selection methods have been proposed, which apply Shannon entropy to measure image information. However, the Shannon entropy is not accurate in measuring image information since it neglects the spatial distribution of pixels and i...

Old master drawings were mostly created step by step in several layers using different materials. To art historians and restorers, examination of these layers brings various insights into the artistic work process and helps to answer questions about the object, its attribution and its authenticity. However, these layers typically overlap and are oftentimes difficult to differentiate with the un...

Hyperspectral images (HSI) classification is a high technical remote sensing software. The purpose is to reproduce a thematic map . The HSI contains more than a hundred hyperspectral measures, as bands (or simply images), of the concerned region. They are taken at neighbors frequencies. Unfortunately, some bands are redundant features, others are noisily measured, and the high dimensionality of...

Spectral unmixing is a critical issue in multi-spectral data processing, which has the ability to identify the constituent components of a pixel. Most of the hyperspectral unmixing current methods are based on Linear Mixture Model (LMM) and have been widely used in many scenarios. However, both the noise contained in the LMM and the requirement of essential prior knowledge strongly limit their ...

WorldView 2 (WV2), launched in September 2009, is a satellite with hyperspatial resolution (∼0.5–2 m) capability for Earth surface observation. It has eight spectral bands with enhanced signal-to-noise ratio to cover the visible-to-near-infrared (V–NIR) domain, thus providing a great potential for remote sensing of coastal ecosystem, in particular, the aquatic environments with shallow bottoms ...

Hyperspectral image compression is an important task in remotely sensed Earth Observation as the dimensionality of this kind of image data is ever increasing. This requires on-board compression in order to optimize the donwlink connection when sending the data to Earth. A successful algorithm to perform lossy compression of remotely sensed hyperspectral data is the iterative error analysis (IEA...

Hyperspectral imaging sensors are promising tools for monitoring crop plants or vegetation in different environments. Information on physiology, architecture or biochemistry of plants can be assessed non-invasively and on different scales. For instance, hyperspectral sensors are implemented for stress detection in plant phenotyping processes or in precision agriculture. Up to date, a variety of...

High−resolution hyperspectral imaging (HSI) provides an abundance of spectral data for feature analysis in image processing. Usually, the amount of information contained in hyperspectral images is excessive and redundant, and data mining for waveband selection is needed. In applications such as fruit and vegetable defect inspections, effective spectral combination and data fusing methods are re...

Independent Component Analysis (ICA) is a multivariate data analysis process largely sudied these last years in the signal processing community for blind source separation. This paper proposes to show the interest of ICA as a tool for unsupervised analysis of hyperspectral images. The commonly used Principal Component Analysis (PCA) is the mean square optimal projection for gaussian data leadin...