Fusion of Target and Shadow Regions for Improved SAR ATR

Choi, Jae-Ho; Lee, Myung-Jun; Jeong, Nam-Hoon; Lee, Geon; Kim, Kyung‐Tae

doi:10.1109/tgrs.2022.3165849

Cited by 20 publications

(14 citation statements)

References 55 publications

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“…The intensity distributions of the target and shadow regions exhibit different characteristics, as illustrated in Figure 1. Therefore, a simple threshold-based method, relying on statistical models, can be employed to separate the target and shadow regions from the SAR image [13,23,44]. Although threshold-based segmentation effectively extracts target regions, it may not be entirely suitable for shadow extraction due to the influence of speckle noise in SAR images and the occlusion caused by other objects.…”

Section: Sar Image Segmentationmentioning

confidence: 99%

“…Anisotropic diffusion filtering can effectively suppress SAR image noise while preserving the structural information of the target and shadow regions. Motivated by [15,44,45], we propose a method for extracting the shadow mask based on the target centroid-labeled. This method first employs anisotropic diffusion filtering to denoise the SAR image, followed by a dual thresholding approach to roughly segment the target and shadow regions.…”

Section: Sar Image Segmentationmentioning

confidence: 99%

“…According to our current understanding, current research on the combination of shadows and deep CNN networks is not in depth. Choi et al [44] proposed a dual-branch CNN structure to separately extract depth features from the preprocessed target region and shadow region. However, this ignores the relative position relationship between the target and shadow.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Target and Shadow Features for SAR Target Recognition

Zhao,

Xue,

Mariam

et al. 2023

Sensors

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Synthetic aperture radar (SAR) sensor often produces a shadow in pairs with the target due to its slant-viewing imaging. As a result, shadows in SAR images can provide critical discriminative features for classifiers, such as target contours and relative positions. However, shadows possess unique properties that differ from targets, such as low intensity and sensitivity to depression angles, making it challenging to extract depth features from shadows directly using convolutional neural networks (CNN). In this paper, we propose a new SAR image-classification framework to utilize target and shadow information comprehensively. First, we design a SAR image segmentation method to extract target regions and shadow masks. Second, based on SAR projection geometry, we propose a data-augmentation method to compensate for the geometric distortion of shadows due to differences in depression angles. Finally, we introduce a feature-enhancement module (FEM) based on depthwise separable convolution (DSC) and convolutional block attention module (CBAM), enabling deep networks to fuse target and shadow features adaptively. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that when only using target and shadow information, the published deep-learning models can still achieve state-of-the-art performance after embedding the FEM.

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Section: Sar Image Segmentationmentioning

confidence: 99%

Section: Sar Image Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integrating Target and Shadow Features for SAR Target Recognition

Zhao,

Xue,

Mariam

et al. 2023

Sensors

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“…The contribution of a coalition is often different from the sum of the individual players due to the influence of player interactions. For example, the shadow region responds to target shape information under specific sensor conditions and can form a coalition with the target region [11], [16]. The BSI can be defined below:…”

Section: B Bivariate Shapley Interactionmentioning

confidence: 99%

Performance Evaluation of Deep Learning Classification Network for Image Features

Yang

Guo

et al. 2021

IEEE Access

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Letters on IEEE Xplore. In recent years, deep learning has been widely used in SAR ATR and achieved excellent performance on the MSTAR dataset. However, due to constrained imaging conditions, MSTAR has data biases such as background correlation, i.e., background clutter properties have a spurious correlation with target classes. Deep learning can overfit clutter to reduce training errors. Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR. Existing methods only qualitatively analyze this phenomenon. In this paper, we quantify the contributions of different regions to target recognition based on the Shapley value. The Shapley value of clutter measures the degree of overfitting. Moreover, we explain how data bias and model bias contribute to noncausality. Concisely, data bias leads to comparable signal-toclutter ratios and clutter textures in training and test sets. And various model structures have different degrees of overfitting for these biases. The experimental results of various models under standard operating conditions on the MSTAR dataset support our conclusions. Index Terms-Synthetic aperture radar (SAR), automatic target recognition (ATR), deep learning, data bias, Shapley value, causality 2 Texture refers to the spatial organization of a set of basic elements [7]. As shown in Fig. 1, the scattering point amplitude has different spatial distributions in the target, clutter, and shadow regions. These spatial distributions create texture signatures of different regions in SAR images.

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“…The real data are inherently dynamic and hence difficult to predict. So far, most state-of-the-art NCTR systems have followed a "closed world" assumption, meaning that the system model is complete and the system can reason using what was observed previously [9][10][11][12][13][14]. However, this assumption is not realistic and leads to fragile systems that can fail at inference time [15].…”

Section: Introductionmentioning

confidence: 99%

Proportional Similarity-Based Openmax Classifier for Open Set Recognition in SAR Images

2022

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Most of the existing Non-Cooperative Target Recognition (NCTR) systems follow the “closed world” assumption, i.e., they only work with what was previously observed. Nevertheless, the real world is relatively “open” in the sense that the knowledge of the environment is incomplete. Therefore, unknown targets can feed the recognition system at any time while it is operational. Addressing this issue, the Openmax classifier has been recently proposed in the optical domain to make convolutional neural networks (CNN) able to reject unknown targets. There are some fundamental limitations in the Openmax classifier that can end up with two potential errors: (1) rejecting a known target and (2) classifying an unknown target. In this paper, we propose a new classifier to increase the robustness and accuracy. The proposed classifier, which is inspired by the limitations of the Openmax classifier, is based on proportional similarity between the test image and different training classes. We evaluate our method by radar images of man-made targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Moreover, a more in-depth discussion on the Openmax hyper-parameters and a detailed description of the Openmax functioning are given.

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Fusion of Target and Shadow Regions for Improved SAR ATR

Cited by 20 publications

References 55 publications

Integrating Target and Shadow Features for SAR Target Recognition

Integrating Target and Shadow Features for SAR Target Recognition

Performance Evaluation of Deep Learning Classification Network for Image Features

Proportional Similarity-Based Openmax Classifier for Open Set Recognition in SAR Images

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