Object-Guided Remote Sensing Image Scene Classification Based on Joint Use of Deep-Learning Classifier and Detector

Abstract

Due to the extremely complex composition of remote sensing scenes, REmote Sensing Image Scene Classification (RESISC) is still a challenging task. To further improve classification accuracy, this article introduces a deep-learning detector into RESISC and proposes to classify remote sensing images according to the detected class-specific signature objects. Inspired by the classification procedure of human vision system, we design a classification framework that utilizes class-specific signature objects of scene classes to guide scene classification. When performing image classification, the proposed framework first adopts a deep-learning classifier to create an initial judgment of the scene class for an image and then determines the scene class based on the class-specific signature objects detected from the image. The proposed method can compete with the state-of-the-art methods on three RESISC benchmark datasets, including NWPU-RESISC45, AID, and OPTIMAL-31.