Effective Multiscale Residual Network With High-Order Feature Representation for Optical Remote Sensing Scene Classification

Abstract

Scene classification of optical remote sensing is a basic but important task because of its broad application in a range of fields. Due to the powerful feature extraction capabilities, convolutional neural networks (CNNs) have been widely used in optical remote sensing scene classification tasks. Despite the remarkable efforts have been achieved, there are still several problems existed, including the effective multiscale feature description for complex scene, rotation, and low interclass diversity problems. In this letter, to address these mentioned problems and construct a powerful CNN for optical remote sensing scene classification, an effective multiscale residual network with a high-order feature representation (MRHNet) is proposed. First, data preprocessing is utilized to adapt the rotation invariance problem. Second, related to the original residual module, a pyramid convolution is introduced to realize the multiscale feature extraction, and then, its feature description ability is further improved by an effective channel attention module. Third, inspired by the tensor decomposition and its completion, a high-order feature representation structure is designed for recovering discriminative fine-scale details into deep layers to solve the low interclass diversity problem. Finally, extensive experiments are carried on two widely used scene classification datasets (e.g., AID and NWPU-RESISC45), and comparing results show that the proposed MRHNet can achieve superior performances.