Ground Target Classification in Noisy SAR Images Using Convolutional Neural Networks

Wang, Jun; Zheng, Tong; Peng, Lei; Bai, Xiangzhi

doi:10.1109/jstars.2018.2871556

Cited by 78 publications

(37 citation statements)

References 37 publications

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“…It is difficult to manually design effective features for SAR image target recognition. Different from the traditional automatic target recognition technology based on artificial design features [7], [8], deep neural networks, especially convolutional neural networks (CNNs), can automatically learn target features for automatic target recognition [9], which reduces the computational cost and improves the recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Shao et al [10] analyzed and compared the performance of different CNNs on the MSTAR [11] dataset based on accuracy, number of parameters, training time and other metrics to verify the superiority of CNNs for SAR image target recognition. Wang et al [12] proposed despeckling and classification coupled CNNs to distinguish multiple categories of ground targets in SAR images with strong and varying speckle. Shang et al [13] proposed deep memory convolution neural networks to alleviate the problem of overfitting caused by insufficient SAR image samples, and their method achieved higher accuracy than several other well-known SAR image classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adversarial Examples for CNN-Based SAR Image Classification: An Experience Study

Huang

Chen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adversarial Examples for CNN-Based SAR Image Classification: An Experience Study

Huang

Chen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…They used the VGG-16 architecture pre-trained on the Imagenet dataset for the feature extraction. Also, a more noise resistant approach was adopted by Wang et al [20] by utilizing despeckling and classification coupled CNNs (DCC-CNN) to distinguish different classes of targets in SAR images that have strong and varying speckles and a detailed analysis was performed. Figure 1 demonstrates the step by step approach taken.…”

Section: Sar Image Classificationmentioning

confidence: 99%

Generation of SAR Images Using Deep Learning

Bhamidipati

Srivatsa

Gowda³

et al. 2020

SN COMPUT. SCI.

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Synthetic aperture radars (SAR) are a type of high-resolution radars that are used for terrestrial and airborne imaging. They use a moving source to simulate a large antenna aperture, thereby providing a high-quality map of the surroundings. They are popular due to their wide variety of applications in airborne naval and defense systems as well as topography and seismology. In defense applications, they are mainly used for reconnaissance and mapping of enemy targets such as armored vehicles, tanks, and runways. In recent times, there has been a heavy need for machine learning algorithms that are capable of automatically recognizing the targets detected by the SAR without human input. Such algorithms demand large-scale labeled datasets for training, validation and testing. Due to the nature of the SAR, acquisition and collection of these labeled datasets is quite a tedious and expensive task requiring a lot of man hours. Thus, alternate methods to produce new SAR image data are needed. In this paper, generative adversarial networks (GAN) are used to generate new spotlight SAR images from a limited pre-existing dataset. To quantitatively verify the effectiveness of the images produced by the GAN, a novel convolutional neural network (CNN) is devised and trained on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset images.

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“…CNNs have shown its great superiority in various image processing, e.g. optical images [17], medical images [18], [19], synthetic aperture radar (SAR) images [20], etc. Therefore, researchers begin to apply deep learning methods to the classification task of LiDAR data.…”

Section: Introductionmentioning

confidence: 99%

Classification of LiDAR Data Combined Octave Convolution With Capsule Network

Cao

Wang

et al. 2020

IEEE Access

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Light Detection and Ranging (LiDAR) data are widely used for high-resolution land cover mapping, which can provide very valuable information about the height of the surveyed area for the discrimination of classes. In order to utilize the advantages of deep models for the classification of LiDAR-derived features, a new classification algorithm combined Octave Convolution (OctConv) with Capsule Network (CapsNet), is proposed here to hierarchically extract robust and discriminant features of the input data, called as OctConv-CapsNet. In the proposed approach, CapsNet captures the spatial information of the data, and OctConv processes separately for high-and low-frequency feature. OctConv is embedded in the primary capsule layer of CapsNet so that the proposed approach can make the most of both the spatial information and the high-and low-frequency information simultaneously. The proposed framework performs experiments on two LiDAR-DSM datasets (i.e. Bayview Park and Recology datasets). The results show that, compared with the traditional deep convolution model, OctConv-CapsNet can improve the classification accuracy of LiDAR-DSM data, and when the number of training samples of the experiment is 800, the classification accuracies reached 96.12% and 96.79% on Bayview Park and Recology datasets, respectively.

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Ground Target Classification in Noisy SAR Images Using Convolutional Neural Networks

Cited by 78 publications

References 37 publications

Adversarial Examples for CNN-Based SAR Image Classification: An Experience Study

Adversarial Examples for CNN-Based SAR Image Classification: An Experience Study

Generation of SAR Images Using Deep Learning

Classification of LiDAR Data Combined Octave Convolution With Capsule Network

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