Based the Morphological Filtering BP Algorithm of SAR Image Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1486-1490
Author(s):  
Ding Ding Jiang ◽  
De Rong Cai ◽  
Qiang Wei
Keyword(s):  
Image Recognition ◽  
Learning Algorithm ◽  
Image Interpretation ◽  
Practical Significance ◽  
Sar Image ◽  
Detection Capability ◽  
Rotation Invariant ◽  
Sar Images ◽  
Diverse Background ◽  
Morphological Filtering

SAR image recognition is an important content of of aviation image interpretation work. In this paper, the characteristics of SAR images a practical significance of morphological filtering neural network model and its adaptive BP learning algorithm. As can be seen through the experimental results, the algorithm can not only adapt to the complex and diverse background environment, and has a displacement of the same continuous moving target detection capability, telescopic invariant and rotation invariant features.

scholarly journals Statistics Learning Network Based on the Quadratic Form for SAR Image Classification

2019 ◽  
Vol 11 (3) ◽  
pp. 282 ◽  
Author(s):  
Chu He ◽  
Bokun He ◽  
Xinlong Liu ◽  
Chenyao Kang ◽  
Mingsheng Liao
Keyword(s):  
Quadratic Form ◽  
Image Classification ◽  
Degrees Of Freedom ◽  
Image Interpretation ◽  
Sar Image ◽  
Statistical Features ◽  
Image Description ◽  
Linear Transformations ◽  
Sar Images ◽  
Learning Network

The convolutional neural network (CNN) has shown great potential in many fields; however, transferring this potential to synthetic aperture radar (SAR) image interpretation is still a challenging task. The coherent imaging mechanism causes the SAR signal to present strong fluctuations, and this randomness property calls for many degrees of freedom (DoFs) for the SAR image description. In this paper, a statistics learning network (SLN) based on the quadratic form is presented. The statistical features are expected to be fitted in the SLN for SAR image representation. (i) Relying on the quadratic form in linear algebra theory, a quadratic primitive is developed to comprehensively learn the elementary statistical features. This primitive is an extension to the convolutional primitive that involves both nonlinear and linear transformations and provides more flexibility in feature extraction. (ii) With the aid of this quadratic primitive, the SLN is proposed for the classification task. In the SLN, different types of statistics of SAR images are automatically extracted for representation. Experimental results on three datasets show that the SLN outperforms a standard CNN and traditional texture-based methods and has potential for SAR image classification.

scholarly journals COLORIZING SENTINEL-1 SAR IMAGES USING A VARIATIONAL AUTOENCODER CONDITIONED ON SENTINEL-2 IMAGERY

2018 ◽  
Vol XLII-2 ◽  
pp. 1045-1051 ◽  
Author(s):  
M. Schmitt ◽  
L. H. Hughes ◽  
M. Körner ◽  
X. X. Zhu
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Single Channel ◽  
Image Interpretation ◽  
Sar Image ◽  
Sar Images ◽  
Variational Autoencoder ◽  
Future Work ◽  
Special Case ◽  
Sentinel 2

In this paper, we have shown an approach for the automatic colorization of SAR backscatter images, which are usually provided in the form of single-channel gray-scale imagery. Using a deep generative model proposed for the purpose of photograph colorization and a Lab-space-based SAR-optical image fusion formulation, we are able to predict artificial color SAR images, which disclose much more information to the human interpreter than the original SAR data. Future work will aim at further adaption of the employed procedure to our special case of multi-sensor remote sensing imagery. Furthermore, we will investigate if the low-level representations learned intrinsically by the deep network can be used for SAR image interpretation in an end-to-end manner.

Discontinuity adaptive SAR image despeckling using curvelet-based BM3D technique

2019 ◽  
Vol 17 (03) ◽  
pp. 1950016 ◽  
Author(s):  
D. Devapal ◽  
S. S. Kumar ◽  
R. Sethunadh
Keyword(s):  
Multiplicative Noise ◽  
Image Interpretation ◽  
Speckle Noise ◽  
Performance Measure ◽  
Block Matching ◽  
Manhattan Distance ◽  
Destructive Interference ◽  
Sar Image ◽  
Sar Images ◽  
Local Means

Synthetic Aperture Radar (SAR) is an all-weather, day and night satellite imaging technology where the radar is mounted on aircraft and successive pulses of radio waves are transmitted to illuminate the target scene. The signal processing of the recorded backscattered echoes produce SAR images. SAR images contain inherent multiplicative speckle noise which is formed due to the constructive and destructive interference of transmitted signals with the returning signals. Speckle noise appears as granular patterns and makes the image interpretation difficult. Non-local means approaches like Block Matching and 3D filtering (BM3D) are effective scheme for removing speckle noise from SAR images. This method gives good performance for additive noise but is not adaptive to curved edges and discontinuities that occur in SAR images affected by multiplicative noise. This paper proposes a three-step refined algorithm to adapt BM3D for despeckling multiplicative speckle noise. In the proposed scheme curvelet is used to find the transform coefficients and this modification in the transform domain improves the despeckling accuracy of BM3D. Also Wiener filtering is replaced with Importance Sampling Unscented Kalman Filtering (ISUKF) for better adapting to discontinuities in the real SAR image. An improved method of grouping is proposed here based on Manhattan distance which better adapts to constantly changing multiplicative noise statistics. A detailed comparative study is carried out on each step using various well-known performance measures. From the results, it is found that the proposed Curvelet-ISUKF-Manhattan BM3D (CIM-BM3D) method of despeckling has better values for all the performance measure and the results are also visually verified.

scholarly journals Unsupervised Learning Method for SAR Image Classification Based on Spiking Neural Network

2021 ◽  
Author(s):  
Jiankun Chen ◽  
Xiaolan Qiu ◽  
Chuanzhao Han ◽  
Yirong Wu
Keyword(s):  
Neural Network ◽  
Unsupervised Learning ◽  
Image Classification ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Image Interpretation ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Sar Image ◽  
The Brain

Recent neuroscience research results show that the nerve information in the brain is not only encoded by the spatial information. Spiking neural network based on pulse frequency coding plays a very important role in dealing with the problem of brain signal, especially complicated space-time information. In this paper, an unsupervised learning algorithm for bilayer feedforward spiking neural networks based on spike-timing dependent plasticity (STDP) competitiveness is proposed and applied to SAR image classification on MSTAR for the first time. The SNN learns autonomously from the input value without any labeled signal and the overall classification accuracy of SAR targets reached 80.8%. The experimental results show that the algorithm adopts the synaptic neurons and network structure with stronger biological rationality, and has the ability to classify targets on SAR image. Meanwhile, the feature map extraction ability of neurons is visualized by the generative property of SNN, which is a beneficial attempt to apply the brain-like neural network into SAR image interpretation.

scholarly journals Self-Matching CAM: A Novel Accurate Visual Explanation of CNNs for SAR Image Interpretation

2021 ◽  
Vol 13 (9) ◽  
pp. 1772
Author(s):  
Zhenpeng Feng ◽  
Mingzhe Zhu ◽  
Ljubiša Stanković ◽  
Hongbing Ji
Keyword(s):  
Target Recognition ◽  
Image Interpretation ◽  
Automatic Target Recognition ◽  
Complex Data ◽  
Sar Image ◽  
Feature Maps ◽  
Sar Images ◽  
Recognition Mechanism ◽  
Imaging Processing ◽  
Activation Mapping

Synthetic aperture radar (SAR) image interpretation has long been an important but challenging task in SAR imaging processing. Generally, SAR image interpretation comprises complex procedures including filtering, feature extraction, image segmentation, and target recognition, which greatly reduce the efficiency of data processing. In an era of deep learning, numerous automatic target recognition methods have been proposed based on convolutional neural networks (CNNs) due to their strong capabilities for data abstraction and mining. In contrast to general methods, CNNs own an end-to-end structure where complex data preprocessing is not needed, thus the efficiency can be improved dramatically once a CNN is well trained. However, the recognition mechanism of a CNN is unclear, which hinders its application in many scenarios. In this paper, Self-Matching class activation mapping (CAM) is proposed to visualize what a CNN learns from SAR images to make a decision. Self-Matching CAM assigns a pixel-wise weight matrix to feature maps of different channels by matching them with the input SAR image. By using Self-Matching CAM, the detailed information of the target can be well preserved in an accurate visual explanation heatmap of a CNN for SAR image interpretation. Numerous experiments on a benchmark dataset (MSTAR) verify the validity of Self-Matching CAM.

Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
Dan Luo
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Supervised Learning ◽  
Image Recognition ◽  
Production Efficiency ◽  
Learning Algorithm ◽  
Electronic Components ◽  
Electron Device ◽  
Actual Recognition ◽  
The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

scholarly journals Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

2021 ◽  
Vol 13 (14) ◽  
pp. 2686
Author(s):  
Di Wei ◽  
Yuang Du ◽  
Lan Du ◽  
Lu Li
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Target Detection ◽  
Scene Recognition ◽  
Attention Mechanism ◽  
Sar Image ◽  
Target Level ◽  
Sar Images ◽  
Training Samples ◽  
Pass Through

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.

scholarly journals High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

2021 ◽  
Vol 13 (2) ◽  
pp. 328
Author(s):  
Wenkai Liang ◽  
Yan Wu ◽  
Ming Li ◽  
Yice Cao ◽  
Xin Hu
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Feature Fusion ◽  
Representation Learning ◽  
Sar Image ◽  
Gabor Filtering ◽  
Feature Maps ◽  
Sar Images ◽  
Multi Scale ◽  
Deep Feature

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

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