Abstract
Satellite scene images contain multiple sub-regions of different land use categories; however, traditional approaches usually classify them into a particular category only. In this paper, a new approach is proposed for automatically analyzing the semantic content of sub-regions of satellite images. At the core of the proposed approach is the recently introduced deep rule-based image classification method. The proposed approach includes a self-organizing set of transparent zero order fuzzy IF THEN rules with human-interpretable prototypes identified from the training images and a pre-trained deep convolutional neural network as the feature descriptor. It requires a very short, nonparametric, highly parallelizable training process and can perform a highly accurate analysis on the semantic features of local areas of the image with the generated IF-THEN rules in a fully automatic way. Examples based on benchmark datasets demonstrate the validity and effectiveness of the proposed approach.