Cloud detection in satellite images using multi-objective social spider optimization

Abstract

Cloud detection algorithms have emerged to automate image data analysis because of its prime influential factor in remote sensing image quality. Cloud detection algorithm still needs domain-expert intervention and large number of training examples to ensure good performance whose acquirement becomes difficult due to unavailability of labeled data as well as the time and process heads involved. The paper puts forward multi-objective social spider optimization (MOSSO) based efficient clustering technique to detect clouds in the visible range. This paper explains the proposed MOSSO algorithm along-with the analysis carried on 14 benchmark two-objective test problems against MOEA/D, MODE, MOPSO and SPEA2 multi-objective algorithms. Further, the strengths and weaknesses of the proposed algorithm are analyzed and have been used for the implementation of an efficient clustering technique named as MOSSO-C. Optimal centroid matrix for clustering is attained in MOSSO-C through environmental selection whose performance evaluation has been done on six synthetic databases and are compared with above mentioned conventional multi-objective algorithms. The obtained results encourage the use of MOSSO-C technique to get labeled data for training process of neural network classifier. This approach efficiently classifies the cloudy pixels against various Earth’s surfaces (water, vegetation and land). The paper also discusses the performance evaluation of proposed technique on four Landsat 8 data which shows on an average 96.37% performance accuracy in detecting cloudy pixels.