Deep learning has attracted increasing attention across a number of disciplines in recent years. In the field of remote sensing, ship detection based on deep learning for synthetic aperture radar (SAR) imagery is replacing traditional methods as a mainstream research method. The multiple scales of ship objects make the detection of ship targets a challenging task in SAR images. This paper proposes a new methodology for better detection of multi-scale ship objects in SAR images, which is based on YOLOv5 with a small model size (YOLOv5s), namely the multi-scale ship detection network (MSSDNet). We construct two modules in MSSDNet: the CSPMRes2 (Cross Stage Partial network with Modified Res2Net) module for improving feature representation capability and the FC-FPN (Feature Pyramid Network with Fusion Coefficients) module for fusing feature maps adaptively. Firstly, the CSPMRes2 module introduces modified Res2Net (MRes2) with a coordinate attention module (CAM) for multi-scale features extraction in scale dimension, then the CSPMRes2 module will be used as a basic module in the depth dimension of the MSSDNet backbone. Thus, our backbone of MSSDNet has the capabilities of features extraction in both depth and scale dimensions. In the FC-FPN module, we set a learnable fusion coefficient for each feature map participating in fusion, which helps the FC-FPN module choose the best features to fuse for multi-scale objects detection tasks. After the feature fusion, we pass the output through the CSPMRes2 module for better feature representation. The performance evaluation for this study is conducted using an RTX2080Ti GPU, and two different datasets: SSDD and SARShip are used. These experiments on SSDD and SARShip datasets confirm that MSSDNet leads to superior multi-scale ship detection compared with the state-of-the-art methods. Moreover, in comparisons of network model size and inference time, our MSSDNet also has huge advantages with related methods.
Sparse representation (SR)-based approaches and collaborative representation (CR)-based methods are proved to be effective to detect the anomalies in a hyperspectral image (HSI). Nevertheless, the existing methods for achieving hyperspectral anomaly detection (HAD) generally only consider one of them, failing to comprehensively exploit them to further promote the detection performance. To address the issue, a novel HAD method, which integrates both sparse representation and collaborative representation (SRCR), is proposed in this paper. To be specific, a SR model, whose overcomplete dictionary is generated by means of the density-based clustering algorithm and superpixel segmentation method, is firstly constructed for each pixel in an HSI. Then, for each pixel in an HSI, the used atoms in SR model are sifted to form the background dictionary corresponding to the CR model. To fully exploit both SR and CR information, we further combine the residual features obtained from both SR and CR model by the nonlinear transformation function to generate the response map. Finally, to preserve contour information of the objects, a postprocessing operation with guided filter is imposed into the response map to acquire the detection result. Experiments conducted on simulated and real data sets demonstrate that the proposed SRCR outperforms the state-of-the-art methods.
Low rank and sparse representation (LRSR) technique has attracted increasing attention for hyperspectral anomaly detection (HAD). Although a large quantity of researches based on LRSR for HAD is proposed, the detection performance is still limited, due to the unsatisfactory dictionary construction and insufficient consideration of global and local characteristics. To tackle above concern, a novel HAD method, termed as dual collaborative constraints regularized low rank and sparse representation via robust dictionaries construction (DCC-LRSR-RDC), is proposed in this paper. Concretely, a robust dictionary construction strategy, which thoroughly excavates the potential of density estimation model and local outlier factor, is proposed to yield pure and representative dictionary atoms. To fully exploit the global and local characteristics of HSI, dual collaborative constraints corresponding to the background and anomaly components are imposed on the LRSR model. Notably, two weighted matrices are further exerted on the representation coefficients to improve the effect of collaborative constraints, considering the fact that the surrounding pixels similar to the testing pixel should be given large weight, otherwise the weight is expected to be small. In this way, the background and anomaly components can be well modeled. Additionally, a nonlinear transformation operation, which combines the output of the density estimation model and local outlier factor with the detection result derived from the LRSR model, is developed to suppress the background. The experiments conducted on one simulated dataset and three real datasets demonstrate the superiority of the proposed method compared with the four typical methods and four state-of-the-art methods.
Recently, the isolation forest (IF) methods have received increasing attention for their promising performance in hyperspectral anomaly detection (HAD). However, limited by the ability of exploiting spatial-spectral information, existing IF-based methods suffer from a lot of false alarms and disappointing performance of detecting local anomalies. To overcome the two problems, a multiscale superpixel guided discriminative forest method is proposed for HAD. First, the multiscale superpixel segmentation is employed to generate some homogeneous regions, and it can effectively extract spatial information to guide anomaly detection for the discriminative forest in local areas. Then, a novel discriminative forest (DF) model with the gain split criterion is designed, which enhances the sensitivity of the DF to local anomalies by the utilization of multi-dimension spectral bands for node division; meanwhile, the acceptable range of hyperplane attribute values is introduced to capture any unseen anomaly pixels that are out-of-range in the evaluation stage. Finally, for the high false alarm rate situation in the existing IF-based algorithms, the multiscale fusion with guided filtering is put forward to refine the initial detection results from the DF. In addition, the extensive experimental results on four real hyperspectral datasets demonstrate the effectiveness of the proposed method.
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