Working Paper Communication Version 1 This version is not peer-reviewed

Mid-Low Resolution Remote Sensing Ship Detection Using Super-Resolved Feature Representation

Version 1 : Received: 15 August 2021 / Approved: 16 August 2021 / Online: 16 August 2021 (12:51:38 CEST)

How to cite: He, S.; Zou, H.; Wang, Y.; Li, R.; Cheng, F.; Cao, X. Mid-Low Resolution Remote Sensing Ship Detection Using Super-Resolved Feature Representation. Preprints 2021, 2021080337 He, S.; Zou, H.; Wang, Y.; Li, R.; Cheng, F.; Cao, X. Mid-Low Resolution Remote Sensing Ship Detection Using Super-Resolved Feature Representation. Preprints 2021, 2021080337

Abstract

Existing methods enhance mid-low resolution remote sensing ship detection by feeding super-resolved images to the detectors. Although these methods marginally improve the detection accuracy, the correlation between image super-resolution (SR) and ship detection is under-exploited. In this paper, we propose a simple but effective ship detection method called ShipSR-Det, in which both the output image and the intermediate features of the SR module are fed to the detection module. Using the super-resolved feature representation, the potential benefit introduced by image SR can be fully used for ship detection. We apply our method to the SSD and Faster-RCNN detectors and develop ShipSR-SSD and ShipSR-Faster-RCNN, respectively. Extensive ablation studies validate the effectiveness and generality of our method. Moreover, we compare ShipSR-Faster-RCNN with several state-of-the-art ship detection methods. Comparative results on the HRSC2016, DOTA and NWPU VHR-10 datasets demonstrate the superior performance of our proposed method.

Keywords

Ship detection; image super-resolution; mid-low resolution remote sensing images

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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