HJ-1A satellite is often used to monitor environmental disaster and plays an important role in environmental changes. Because of the affection of various factors, certain band of HJ-1A hyperspectral remote sensing data is severe loss or distortion, which brings great difficulties for subsequent quantitative processing. A novel adaptive window matching algorithm, which can adjust intelligently s...

An important aspect of spectral image analysis is identification of materials present in the object or scene being imaged. Enabling technologies include image enhancement, segmentation and spectral trace recovery. Since multi-spectral or hyperspectral imagery is generally low resolution, it is possible for pixels in the image to contain several materials. Also, noise and blur can present signif...

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...

The maximum noise fraction (MNF) transform, which produces the improved order of components by signal to noise ratio (SNR), has been commonly used for spectral feature extraction from hyperspectral remote sensing data before image classification. When hyperspectral data contains a spectral distortion, also known as a “smile” property, the first component of the MNF, which should have high image...

We propose a method for measuring and quantifying image quality in push-broom hyperspectral cameras in terms of spatial misregistration caused by keystone and variations in the point spread function (PSF) across spectral channels, and image sharpness. The method is suitable for both traditional push-broom hyperspectral cameras where keystone is corrected in hardware and cameras where keystone i...

Y. Altmann, N. Brun , N. Dobigeon , K. March, S. Moussaoui, O. Schneegans 1School of Engineering and Physical Sciences, Heriot-Watt University Earl Mountbatten Building, Riccarton, EH14 4AS, Edinburgh, U.K. Laboratoire de Physique des Solides, CNRS UMR 8502, Univ. Paris-Sud, Univ. Paris-Saclay Bât. 510, 91405 Orsay Cedex, France University of Toulouse, IRIT/INP-ENSEEIHT/TéSA 2 rue Charles Camic...

Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of thr...

A LMOST A DECADE after the milestone special issue of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) dedicated to the analysis of hyperspectral image data, edited by Prof. Landgrebe, Prof. Serpico, Prof. Crawford, and Prof. Singhroy [1], it is a great pleasure to introduce this new special issue on hyperspectral image and signal processing. In the intervening years, interest in h...

The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of unmixing algorithms. formulation existing HSIs is relatively complex would change conjunction with different devices imaging settings. For real applications, applying denoising approaches without accurate close-to-reality modeling before may not improve, but rather degrade performanc...

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set ...