A Component-Based Multi-Layer Parallel Network for Airplane Detection in SAR Imagery

Abstract

In this paper, a component-based multi-layer parallel network is proposed for airplane detection in Synthetic Aperture Radar (SAR) imagery. In response to the problems called sparsity and diversity brought by SAR scattering mechanism, depth characteristics and component structure are utilized in the presented algorithm. Compared with traditional features, the depth characteristics have better description ability to deal with diversity. Component information is contributing in detecting complete targets. The proposed algorithm consists of two parallel networks and a constraint layer. First, the component information is introduced into the network by labeling. Then, the overall target and corresponding components are detected by the trained model. In the following discriminative constraint layer, the maximum probability and prior information are adopted to filter out wrong detection. Experiments for several comparative methods are conducted on TerraSAR-X SAR imagery; the results indicate that the proposed network has a higher accuracy for airplane detection.

Attention Receptive Pyramid Network for Ship Detection in SAR Images

“…Same as other classical two-stage object detection networks, RPN and the final prediction networks are all optimized by using multitask loss, which is given by (9). N cls and N reg refer to numbers of a minibatch samples in the training phase.…”

“…observation [3] and so on. As one of the important applications, synthetic aperture radar automatic target recognition (SAR ATR) aims to figure out locations and class labels of potential targets and has been researched for a long time [4]- [9]. In this field, an important branch is ship detection in SAR images.…”

SAR Target Classification Based on Integration of ASC Parts Model and Deep Learning Algorithm

“…This structure is widely used in the research of multi-view SAR images [28]. At present, there are three main research targets of deep-learning based SAR image target recognition methods: aircraft target [29], ship target [30], and vehicle target [31]. All these researches need the support of abundant target data.…”

Coupling Denoising to Detection for SAR Imagery

“…With the recent rapid development of deep learning, many deep convolutional neural network (CNN)-based object detection approaches using SAR imagery have gained increased attention. The successes of the deep detectors on SAR images facilitate a wide range of civil and military applications, such as detection of ship [1][2][3][4][5], aircraft [6][7][8][9], destroyed building [10], oceanic internal wave [11], oceanic eddy [12], oil spill [13], avalanche [14], and trough [15]. For the further research purposes, several SAR object detection datasets have also been released called AIR-SARShip-1.0 [16], SAR-Ship-Dataset [17], SAR ship detection dataset (SSDD) [18], and HRSID [19].…”