scholarly journals SAR Image Change Detection Based on Multi-scale Feature Extraction

2021 ◽  
Vol 10 (4) ◽  
pp. 077-081
Author(s):  
Xiaoqian Yuan ◽  
Chao Chen ◽  
Shan Tian ◽  
Jiandan Zhong
Keyword(s):  
Feature Extraction ◽  
Change Detection ◽  
Speckle Noise ◽  
Kernel Matrix ◽  
Sar Image ◽  
Scale Feature ◽  
Difference Image ◽  
Multi Scale ◽  
Image Change Detection ◽  
The Difference

In order to improve the contrast of the difference image and reduce the interference of the speckle noise in the synthetic aperture radar (SAR) image, this paper proposes a SAR image change detection algorithm based on multi-scale feature extraction. In this paper, a kernel matrix with weights is used to extract features of two original images, and then the logarithmic ratio method is used to obtain the difference images of two images, and the change area of the images are extracted. Then, the different sizes of kernel matrix are used to extract the abstract features of different scales of the difference image. This operation can make the difference image have a higher contrast. Finally, the cumulative weighted average is obtained to obtain the final difference image, which can further suppress the speckle noise in the image.

scholarly journals A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images

2020 ◽  
Vol 12 (10) ◽  
pp. 1619 ◽  
Author(s):  
Jia-Wei Chen ◽  
Rongfang Wang ◽  
Fan Ding ◽  
Bo Liu ◽  
Licheng Jiao ◽  
...  
Keyword(s):  
Change Detection ◽  
Sar Image ◽  
Sar Images ◽  
Convolutional Network ◽  
Difference Image ◽  
Multi Scale ◽  
Spatial Pooling ◽  
Testing Dataset ◽  
Image Change Detection ◽  
The Difference

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a multi-scale spatial pooling (MSSP) network to exploit the changed information from the noisy difference image. Being different from the traditional convolutional network with only mono-scale pooling kernels, in the proposed method, multi-scale pooling kernels are equipped in a convolutional network to exploit the spatial context information on changed regions from the difference image. Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset. We compare the proposed method with other state-of-arts and the comparisons are performed on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that our proposed method obtain comparable results with S-PCA-Net on YR-A and YR-B dataset and outperforms other state-of-art methods, especially on the Sendai-A and Sendai-B datasets with more complex scenes. More important, MSSP-Net is more efficient than S-PCA-Net and convolutional neural networks (CNN) with less executing time in both training and testing phases.

scholarly journals Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

2020 ◽  
Vol 12 (3) ◽  
pp. 548 ◽  
Author(s):  
Xinzheng Zhang ◽  
Guo Liu ◽  
Ce Zhang ◽  
Peter M. Atkinson ◽  
Xiaoheng Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Speckle Noise ◽  
Sar Image ◽  
Phase Object ◽  
Sar Images ◽  
Two Phase ◽  
Object Based ◽  
Multi Temporal ◽  
Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

scholarly journals Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

2021 ◽  
Vol 13 (18) ◽  
pp. 3697
Author(s):  
Liangliang Li ◽  
Hongbing Ma ◽  
Zhenhong Jia
Keyword(s):  
Neural Networks ◽  
Change Detection ◽  
Saliency Detection ◽  
Speckle Noise ◽  
Saliency Map ◽  
Wavelet Neural Networks ◽  
Sar Images ◽  
Difference Image ◽  
The Difference ◽  
Log Ratio

Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.

Using Combined Difference Image and $k$ -Means Clustering for SAR Image Change Detection

2014 ◽  
Vol 11 (3) ◽  
pp. 691-695 ◽  
Author(s):  
Yaoguo Zheng ◽  
Xiangrong Zhang ◽  
Biao Hou ◽  
Ganchao Liu
Keyword(s):  
Change Detection ◽  
Sar Image ◽  
Difference Image ◽  
Image Change Detection

scholarly journals Urban Planning Image Feature Enhancement and Simulation Based on Partial Differential Equation Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Duo Li
Keyword(s):  
Partial Differential Equations ◽  
Path Planning ◽  
Differential Equations ◽  
Change Detection ◽  
Dimensional Space ◽  
Speckle Noise ◽  
Planning Problem ◽  
Partial Differential ◽  
Difference Image ◽  
The Difference

Based on the introduction of the basic ideas and related technologies of partial differential equations, as well as the method of path planning, the application of partial differential equations in solving urban path planning is studied. The path planning model of partial differential equations and the setting of obstacle boundary conditions are introduced, and adaptive. Theoretical research and experimental results show that it is feasible and effective to solve urban path planning by partial differential equations, which provides a new way for urban path planning research ideas and methods. This paper proposes an image detection algorithm based on diffusion equation. According to the logarithmic transformation, the multiplicative speckle noise in the image can be converted into additive noise. We first perform logarithmic transformation on the image and then use the denoising model of the diffusion equation to filter out the noise in the image and then use the logarithm to recognize the image. The difference image is obtained by the domain difference method, and finally, the difference image is classified by the clustering algorithm, and the change area is extracted. Experiments show that the algorithm can effectively reduce the effect of multiplicative speckle noise on the change detection results, improve the accuracy of change detection, and shorten the change detection time. This article takes the path planning problem of a two-dimensional space city as an example to discuss the application of partial differential equations. According to the principle of energy conservation, this paper uses the two-dimensional space radiant heat conduction equation as an example to model and illustrate the solution of the path planning problem.

scholarly journals Fast SAR Image Change Detection Using Bayesian Approach Based Difference Image and Modified Statistical Region Merging

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Han Zhang ◽  
Weiping Ni ◽  
Weidong Yan ◽  
Hui Bian ◽  
Junzheng Wu
Keyword(s):  
Change Detection ◽  
Bayesian Approach ◽  
Generation Process ◽  
Sar Image ◽  
Region Merging ◽  
Difference Image ◽  
Leibler Divergence ◽  
Image Change Detection ◽  
Log Ratio ◽  
Statistical Region Merging

A novel fast SAR image change detection method is presented in this paper. Based on a Bayesian approach, the prior information that speckles follow the Nakagami distribution is incorporated into the difference image (DI) generation process. The new DI performs much better than the familiar log ratio (LR) DI as well as the cumulant based Kullback-Leibler divergence (CKLD) DI. The statistical region merging (SRM) approach is first introduced to change detection context. A new clustering procedure with the region variance as the statistical inference variable is exhibited to tailor SAR image change detection purposes, with only two classes in the final map, the unchanged and changed classes. The most prominent advantages of the proposed modified SRM (MSRM) method are the ability to cope with noise corruption and the quick implementation. Experimental results show that the proposed method is superior in both the change detection accuracy and the operation efficiency.

scholarly journals Multipolarimetric SAR image change detection based on multiscale feature-level fusion

2015 ◽  
Vol XL-7/W4 ◽  
pp. 155-158
Author(s):  
X. Sun ◽  
J. Zhang ◽  
L. Zhai
Keyword(s):  
Change Detection ◽  
Low Frequency ◽  
Speckle Noise ◽  
Contourlet Transform ◽  
Sar Image ◽  
Nonsubsampled Contourlet Transform ◽  
Feature Level Fusion ◽  
Image Change Detection ◽  
The Rich ◽  
Level Fusion

Many methodologies of change detection have been discussed in the literature, but most of them are tested on only optical images or traditional synthetic-aperture radar (SAR) images. Few studies have investigated multipolarimetric SAR image change detection. In this study, we presented a type of multipolarimetric SAR image change detection approach based on nonsubsampled contourlet transform and multiscale feature-level fusion techniques. In this approach, Instead of denoising an image in advance, the nonsubsampled contourlet transform multiscale decomposition was used to reduce the effect of speckle noise by processing only the low-frequency sub-band coefficients of the decomposed image, and the multiscale feature-level fusion technique was employed to integrate the rich information obtained from various polarization images. Because SAR image information is dependent on scale, a multiscale multipolarimetric feature-level fusion strategy is introduced into the change detection to improve change detection precision; this feature-level fusion can not only achieve complementation of information with different polarizations and on different scales, but also has better robustness against noise. Compared with PCA methods, the proposed method constructs better differential images, resulting in higher change detection precision.

scholarly journals UNSUPERVISED CHANGE DETECTION IN SAR IMAGES USING GAUSSIAN MIXTURE MODELS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 407-410 ◽  
Author(s):  
E. Kiana ◽  
S. Homayouni ◽  
M. A. Sharifi ◽  
M. Farid-Rohani
Keyword(s):  
Change Detection ◽  
Gaussian Mixture Models ◽  
Speckle Noise ◽  
Gaussian Mixture ◽  
Sar Images ◽  
Difference Image ◽  
Negative Change ◽  
Aerial Vehicle ◽  
The Difference ◽  
Speckle Filtering

In this paper, we propose a method for unsupervised change detection in Remote Sensing Synthetic Aperture Radar (SAR) images. This method is based on the mixture modelling of the histogram of difference image. In this process, the difference image is classified into three classes; negative change class, positive change class and no change class. However the SAR images suffer from speckle noise, the proposed method is able to map the changes without speckle filtering. To evaluate the performance of this method, two dates of SAR data acquired by Uninhabited Aerial Vehicle Synthetic from an agriculture area are used. Change detection results show better efficiency when compared to the state-of-the-art methods.

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