COMPRESSIVE SENSING APPROACH TO HYPERSPECTRAL IMAGE COMPRESSION

Hyperspectral image (HSI) processing is one of the key processes in satellite imaging applications. Hyperspectral imaging spectrometers collect huge volumes of data since the image is captured across different wavelength bands in the electromagnetic spectrum. As a result, compression of hyperspectral images is one of the active area in research community from many years. The research work proposes a new compressive sensing based approach for the compression of hyperspectral images called SHSIR (Sparsification of hyperspectral image and reconstruction). The algorithm computes the coefficients of fractional abundance map in matrix setup, which is used to reconstruct the hyperspectral image. To optimize the problem with non-smooth term existence along with large dimensionality, Bregman iterations method of multipliers is used, which converts the difficult optimization problem into simpler cyclic sequence problem. Experimental result demonstrates the supremacy of the proposed method over other existing techniques

Assessing skin lesion evolution from multispectral image sequences

During the evaluation of skin disease treatments, dermatologists have to clinically measure the evolution of the pathology severity of each patient during treatment periods. Such a process is sensitive to intra- and inter- dermatologist diagnosis. To make this severity measurement more objective we quantify the pathology severity using a new image processing based method. We focus on a hyperpigmentation disorder called melasma. During a treatment period, multispectral images are taken on patients receiving the same treatment. After co-registration and segmentation steps, we propose an algorithm to measure the intensity, the size and the homogeneity evolution of the pathological areas. Obtained results are compared with a dermatologist diagnosis using statistical tests on two clinical studies containing respectively 384 images from 16 patients and 352 images from 22 patients.This research report is an update of the report 8136. It describes methods and experiments in more details and provides more references.Lors de l'évaluation des traitements des maladies de peau, les dermatologues doivent mesurer la sévérité de la pathologie de chaque patient tout au long d'une période de traitement. Un tel procédé est sensible aux variations intra- et inter- dermatologues. Pour rendrecette mesure de sévérité plus robuste, nous proposons d'utiliser l'imagerie spectrale. Nous nous concentrons sur une pathologie d'hyperpigmentation cutanée appelée mélasma. Au cours d'une période de traitement, des images multispectrales sont acquises sur une population de patients sous traitement. Après des étapes de recalage des séries temporelles d'images et de classification des régions d'intérêt, nous proposons une méthodologie permettant de mesurer, dans le temps, la variation de contraste, de surface et d'homogénéité de la zone pathologique pour chaque patient. Les résultats obtenus sont comparés à un diagnostique clinique à l'aide de tests statistiques réalisés sur une étude clinique complète.Ce rapport de recherche est un complément du rapport de recherche 8136, afin de compléter la bibliographie, et de décrire plus en détail les méthodes et résultat

Random N-Finder (N-FINDR) Endmember Extraction Algorithms for Hyperspectral Imagery

N-finder algorithm (N-FINDR) has been widely used in endmember extraction. When it comes to implementation several issues need to be addressed. One is determination of endmembers, p required for N-FINDR to generate. Another is its computational complexity resulting from an exhaustive search. A third one is its requirement of dimensionality reduction. A fourth and probably the most critical issue is its use of random initial endmembers which results in inconsistent final endmember selection and results are not reproducible. This paper re-invents the wheel by re-designing the N-FINDR in such a way that all the above-mentioned issues can be resolved while making the last issue an advantage. The idea is to implement the N-FINDR as a random algorithm, called random N-FINDR (RN-FINDR) so that a single run using one set of random initial endmembers is considered as one realization. If there is an endmember present in the data, it should appear in any realization regardless of what random set of initial endmembers is used. In this case, the N-FINDR is terminated when the intersection of all realizations produced by two consecutive runs of RN-FINDR remains the same in which case the p is then automatically determined by the intersection set without appealing for any criterion. In order to substantiate the proposed RN-FINDR custom-designed synthetic image experiments with complete knowledge are conducted for validation and real image experiments are also performed to demonstrate its utility in applications