Clustering of Remote Sensing Imagery Using a Social Recognition-Based Multi-objective Gravitational Search Algorithm

Cognitively inspired swarm intelligence algorithms (SIAs) have attracted much attention in the research area of clustering since it can give machine the ability of self-learning to achieve better classification results. Recently, the SIA-based multi-objective optimization (MOO) methods have shown their superiorities in data clustering. However, their performances are limited when applying to the clustering of remote sensing imagery (RSI). To construct an excellent MOO-based clustering method, this paper presents a social recognition-based multi-objective gravitational search algorithm (SMGSA) to achieve simultaneous optimization of two conflicting cluster validity indices, i.e., the Xie-Beni (XB) index and the J_m index. In the SMGSA, searching particles not only are guided by those elite particles stored in an external archive by the gravitational force but also learn from the social recognition of the whole population through the position difference. SMGSA thereby formed with outstanding exploitation ability. Comparison experiments on two public RSI data sets, including a moderate aerial image and a hyperspectral, validated that the MOO-based clustering methods could obtain more accurate results than the single validity index-based method. Moreover, the SMGSA-based method can achieve superior results than that of the multi-objective gravitational search algorithm without social recognition ability. The proposed SMGSA performs favorable balance between the two conflicting cluster validity indices and achieves preferable classification for the RSI. In addition, this study indicates that the swarm intelligence-based cognitive computing is potential for the intelligent interpretation and understanding of complicated remote sensing scene.