Efficient parallel algorithm for pixel classification in remote sensing imagery |
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Authors: | Ujjwal Maulik Anasua Sarkar |
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Affiliation: | (1) Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India;(2) LaBRI, University of Bordeaux1, Talence, 33400, France |
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Abstract: | An important approach for image classification is the clustering of pixels in the spectral domain. Fast detection of different
land cover regions or clusters of arbitrarily varying shapes and sizes in satellite images presents a challenging task. In
this article, an efficient scalable parallel clustering technique of multi-spectral remote sensing imagery using a recently
developed point symmetry-based distance norm is proposed. The proposed distributed computing time efficient point symmetry
based K-Means technique is able to correctly identify presence of overlapping clusters of any arbitrary shape and size, whether
they are intra-symmetrical or inter-symmetrical in nature. A Kd-tree based approximate nearest neighbor searching technique
is used as a speedup strategy for computing the point symmetry based distance. Superiority of this new parallel implementation
with the novel two-phase speedup strategy over existing parallel K-Means clustering algorithm, is demonstrated both quantitatively
and in computing time, on two SPOT and Indian Remote Sensing satellite images, as even K-Means algorithm fails to detect the
symmetry in clusters. Different land cover regions, classified by the algorithms for both images, are also compared with the
available ground truth information. The statistical analysis is also performed to establish its significance to classify both
satellite images and numeric remote sensing data sets, described in terms of feature vectors. |
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