Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 10; p. 2523
• Main Authors: Gao, Weihua, Niu, Wenlong, Wang, Pengcheng, Li, Yanzhao, Ren, Chunxu, Peng, Xiaodong, Yang, Zhen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2023
• Subjects: Algorithms; Disturbances; False alarms; Feature extraction; low SNR; Mathematical analysis; Mathematical models; Methods; moving point target; Moving targets; Neural networks; Remote sensing; Signal to noise ratio; skip connection; Sparsity; Target detection; temporal profile; transient disturbance; Taiwan
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15102523

Moving target detection in optical remote sensing is important for satellite surveillance and space target monitoring. Here, a new moving point target detection framework under a low signal-to-noise ratio (SNR) that uses an end-to-end network (1D-ResNet) to learn the distribution features of transient disturbances in the temporal profile (TP) formed by a target passing through a pixel is proposed. First, we converted the detection of the point target in the image into the detection of transient disturbance in the TP and established mathematical models of different TP types. Then, according to the established mathematical models of TP, we generated the simulation TP dataset to train the 1D-ResNet. In 1D-ResNet, the structure of CBR-1D (Conv1D, BatchNormalization, ReLU) was designed to extract the features of transient disturbance. As the transient disturbance is very weak, we used several skip connections to prevent the loss of features in the deep layers. After the backbone, two LBR (Linear, BatchNormalization, ReLU) modules were used for further feature extraction to classify TP and identify the locations of transient disturbances. A multitask weighted loss function to ensure training convergence was proposed. Sufficient experiments showed that this method effectively detects moving point targets with a low SNR and has the highest detection rate and the lowest false alarm rate compared to other benchmark methods. Our method also has the best detection efficiency.