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

Abstract

The compressive sensing (CS)-based optical remote sensing (ORS) imaging system has been verified for the feasibility through numerical simulation experiments. The CS-based ORS imaging system can reduce the demand for sampling equipment, 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 article proposes an innovation pipeline to perform ship detection tasks, i.e., 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 convolutional neural network-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). First, 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. Second, 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.

Keywords

• Compressive sensing (CS)
• convolutional neural network (CNN)
• joint training optimization
• ship detection based on compressive sensing measurements