{"title":"Blind unmixing of remote sensing data with some pure pixels: Extension and comparison of spatial methods exploiting sparsity and nonnegativity properties","authors":"M. S. Karoui, Y. Deville, S. Hosseini, A. Ouamri","doi":"10.1109/WOSSPA.2013.6602334","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602334","url":null,"abstract":"Multispectral and hyperspectral imaging systems are among the most powerful tools in the field of remote sensing. In remote sensing imagery, pixel values are often linear mixtures of contributions from pure materials contained in the observed scene. In this paper, we extend our recently developed spatial methods for blindly unmixing each pixel of remote sensing data with some pure pixels and we compare their performance, both for multispectral and hyperspectral images. These extended methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA) and nonnegativity constraints. Spatial correlation-based or variance-based SCA algorithms (which detect a few pure-pixel zones) are firstly used to identify the mixing matrix by means of two different approaches for selecting the columns of this matrix. Nonnegative least squares (NLS) or nonnegative matrix factorization (NMF) methods are then used to extract spatial sources. Experiments based on realistic synthetic data are performed to compare the accuracies and the computational costs of these extended methods. We show that the tested methods yield high accuracy with low computational cost for the variance-based methods as compared to those based on correlation.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125962008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adaptive edge detection using ant colony","authors":"K. Benhamza, H. Merabti, Hamid Seridi","doi":"10.1109/WOSSPA.2013.6602361","DOIUrl":"https://doi.org/10.1109/WOSSPA.2013.6602361","url":null,"abstract":"In this paper, an adaptive edges detection method based on ant colony algorithm is presented. Ant colony algorithm is a swarm-based metaheuristic inspired by the self-organizing properties of ant colony in nature. Artificial ants in movement create a pheromone graph, which denotes data of edge image. Further behaviors were added to each ant in response to local stimuli: the ant can self-reproduce and lead its progenitors in an appropriate direction to enhance research in suitable areas and it can die too if it exceeds a specific age and so eliminate the ineffective search. Experimental results show the performance of this technique enriched with these behaviors. It provides a good segmentation, fast and adaptive in extracting edges for a variety of images.","PeriodicalId":417940,"journal":{"name":"2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129749014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}