Object Guided Remote Sensing Image Scene Classification Based on Joint Use of Deep Learning Classifier and Detector
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.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing