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

• Main Authors: Xiaoliang Yang, Weidong Yan, Weiping Ni, Xifeng Pu, Han Zhang, Maoyu Zhang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Class-specific signature object; deep-learning detector; remote sensing image; scene classification
• Online Access: https://ieeexplore.ieee.org/document/9103279/
• ISSN: 2151-1535

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.