RSSM-Net: Remote Sensing Image Scene Classification Based on Multi-Objective Neural Architecture Search

The deep learning (DL)-based scene classification methods have been obtained the remarkable attention for the high spatial resolution remote sensing (HRS) imagery. However, from one aspect, the existing DL methods in HRS image scene classification are usually the variations of the natural image processing methods and often the inherent network structures; from another aspect, the strenuous and significant efforts have been devoted to the design of relevant network structures by human experts. In this paper, learning from the natural evolution, the deep neural network is expected to be globally evolved by the machine for automatically adapting the structure of the HRS imagery, a multi-objective neural architecture search based HRS image scene classification method is proposed (RSSM-Net). The two objectives of minimizing a classification error and the computational complexity have been simultaneously optimized through the evolutionary multi-objective method, the competitive neural architectures in a Pareto solution set are then obtained. The effectiveness is proved by the experiment of the UC Merced dataset with several networks designed by human experts.