Two-Stage Sea-Surface Small Target Detection Using Multifeature-Fusion-Based Binary Classifier and SCR Enhancement

Sea-surface small target detection is challenging for the reason that the small target with low signal-to-clutter ratio (SCR) would be submerged by strong sea spikes in the complex marine environment. Against this background, a two-stage detection method using multifeature-fusion-based binary classifier and SCR enhancement is proposed in this article. In stage 1, the binary classifier extracts multifeatures in the temporal, frequency and time-frequency domains using convolutional neural networks (CNNs) and performs weighted feature fusion by minimizing cross entropy loss. Afterward, in stage 2, SCR enhancement is divided into two steps, which are clutter suppression by modified orthogonal projection (MOP) and interframe incoherent accumulation by Kalman filter, respectively. Specifically, according to the range cells classified as clutter in stage 1, MOP is performed in the range-Doppler (RD) domain to improve SCR by using the projection operator composed by main clutter components via first-order difference method. Then, Kalman filter combines the predict state after RD compensation and observation state with the gain coefficient to realize target energy accumulation and SCR stabilization. Finally, the final decision is made by the constant false alarm rate (CFAR) detector. The simulation results indicate that the proposed binary classifier attains a significant binary classification performance improvement, laying foundations for effective sea clutter suppression and SCR enhancement so as to improve the final detection performance of target location and Doppler frequency.