Ship Detection Based on Compressive Sensing Measurements of Optical Remote Sensing Scenes

Abstract

The compressive sensing (CS)-based optical remote sensing (ORS) imaging system have been verified the feasibility of through numerical simulation experiments, which can reduce the demand for sampling equipment, effectively reduce sampling data, save storage space, and reduce transmission costs. However, it needs to reconstruct the original scene when facing the task of ship detection. The scene reconstruction process of CS is computationally expensive, high memory demanding, and time-consuming. In response to this problem, this paper proposes an innovation pipeline to perform ship detection tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , directly performing ship detection on CS measurements obtained by the imaging system, which avoids the process of scene reconstruction. To achieve the ship detection of CS measurements in the pipeline, we design a CNN-based algorithm, CS-CenterNet, which jointly optimizes the scene compression sampling phase and the measurements' ship detection phase. CS-CenterNet is divided into convolution measurement layer (CML), optimized hourglass network (OHgN), and optimized three-branch head network (OTBHN). Firstly, CML without bias or activation function simulates the block compression sampling process in CS-based ORS imaging system, which performs convolutional coding on the scene to obtain the measurements. Secondly, OHgN extracts high-resolution feature information of measurements. Finally, OTBHN performs heat-map prediction, center-point offset prediction, and width-height prediction. We test the performance of CS-CenterNet using the HRSC2016 and LEVIR datasets. The experimental results show that the algorithm can achieve high-accuracy ship detection based on CS measurements of ORS scenes.