Browsing by Author "Al Moghalis M.A."

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Spatial-spectral based hyperspectral image clustering - An adaptive approach using cluster's bands box-plots
(IADIS, 2014) Al Moghalis M.A.; Hegazy O.M.; Imam I.F.; El-Bastawessy A.H.; Department of Information Systems; College of Computer and Information Systems; Alyamamah University; Saudi Arabia; Department of Information Systems; Faculty of Computers and Information; Cairo University; Egypt; Department of Computer Science; College of Computing and Information Technology; Arab Academy for Science; Technology and Maritime Transport; Egypt; Department of Information Systems; Faculty of Computer Science; MSA University; Egypt
Remote sensing Hyperspectral Image (HSI) has attracted researchers as a rich source of information. A recent trend in HSI analysis directs research to combine features of spatial nature with spectral nature. The motivation was that contiguous pixels share spectral features due to the low spatial resolution. This paper introduces an incremental work to a proposed approach that clusters HSI using K-means and combines spatial and spectral features together. The proposed approach uses selects pixels neighborhood, named kernel, from HSI to represent a cluster. For each cluster, pixels' bands box-plots profile is built to represent cluster bands distribution. Hence, the profile interprets the spectral features of a cluster. The approach starts by selecting K kernels from the given image scene randomly. However, results are affected by this random selection. In this paper, kernels are selected randomly but, they will be subjected to a test that avoids negative effect of randomness and prevents clusters to be represented again. In other words, the test ensures that successive kernels are different than previous selected kernels. Successive kernels are mapped against previous kernels box-plots profiles. Only kernels that prove to be spectrally different are used to represent consecutive clusters centroids. A pixel will join the cluster which score the minimum number of outlier reflectance values. Copyright � 2014 IADIS Press All rights reserved.
Spatial-spectral hyperspectral image clustering using cluster's bands box-plots
(IADIS, 2014) Al Moghalis M.A.; Hegazy O.M.; Imam I.F.; El-Bastawessy A.H.; Dep. of Information Systems; College of Computer and Information Systems; Alyamamah University; KSA; Saudi Arabia; Dep. of Information Systems; Faculty of Computers and Information; Cairo University; Egypt; Dep. of Computer Science; College of Computing and Information Technology; Arab Academy for Science; Technology and Maritime Transport; Egypt; Dea. of Faculty of Computer Science; MSA University; Egypt
The attention given recently for Hyperspectral Images (HSI) in remote sensing was due to its spectral nature used in earth surface exploration. Recently, researches proposed approaches that combine features of spatial nature with spectral nature to enhance HSI analysis accuracy. The reason behind this orientation is that contiguous pixels mostly share spectral features due to the low spatial resolution. In this paper, authors follow this orientation in clustering HSI using K-means. The proposed approach uses kernels, group of spatially neighbored pixels, to build profile for each cluster. The profile preserves cluster's spectral nature through its bands' Box-Plots that were extracted from selected kernels. The approach starts by selecting K kernels from the given image scene randomly instead of generating random kernels. Then a profile for each cluster is built using the selected kernel pixels' spectrums. The profile consists of b box-plots, where b is the number of bands. Each band box-plot interprets the spread of contiguous pixels' reflectance values in that band. Each pixel in the image will join the nearest cluster. The distance to be measured between any given pixel spectrum and cluster's centroid is replaced by counting how many reflectance values have been considered outlier to their corresponding band's box-plot in that cluster profile. The pixel will join the cluster with minimum outlier count. The profiles are updated iteratively using the new pixels distribution. Data sets used in the experiments are captured by Hyperion Earth Observer 1 (EO-1) sensor. Copyright � 2014 IADIS Press All rights reserved.