Improved framework of many-objective evolutionary algorithm to handle cloud detection problem in satellite imagery

Automatic cloud detection algorithm based on supervised learning approach has emerged due to its effectiveness in extracting weather information in satellite images. However, algorithm requires field-expert intervention with huge database of training samples to evaluate its clustering performance. Moreover, lacking in availability of labelled data makes difficult to train the input samples. Therefore, this article puts forward unsupervised many-objective evolutionary clustering technique to discriminate cloudy regions on varying characteristic of underlying surfaces. The study begins with the modification in search capability of theta-NSGA-III optimisation algorithm by incorporating penalised vector angle concept in associate operator. The analysis of proposed approach has been carried out on benchmark many-objective DTLZ test problems, compared against original theta-NSGA-III and NSGA-III algorithms. The proposed modified theta-NSGA-III has been further utilised as clustering technique to solve unsupervised cloud detection problem. Optimal centroid vector for clustering using proposed approach is obtained through modified crossover operator, mutation operator and environmental selection method. Experimental results reveal that proposed approach outperforms comparative many-objective algorithms, MOEA/D and NSGA-III for Landsat 8, MODIS and NOAA satellite images with lower classification average error of 2.44% in cloud detection for most of the evaluated test cases.