scholarly journals A Spatial-Temporal Adaptive Neighborhood-Based Ratio Approach for Change Detection in SAR Images

2018 ◽  
Vol 10 (8) ◽  
pp. 1295 ◽  
Author(s):  
Huifu Zhuang ◽  
Hongdong Fan ◽  
Kazhong Deng ◽  
Guobiao Yao
Keyword(s):  
Change Detection ◽  
Window Size ◽  
Speckle Noise ◽  
Adaptive Strategy ◽  
Negative Influence ◽  
Sar Images ◽  
Spatial Homogeneity ◽  
Difference Image ◽  
Negative Effect ◽  
Adaptive Neighborhood

The neighborhood-based method was proposed and widely used in the change detection of synthetic aperture radar (SAR) images because the neighborhood information of SAR images is effective to reduce the negative effect of speckle noise. Nevertheless, for the neighborhood-based method, it is unreasonable to use a fixed window size for the entire image because the optimal window size of different pixels in an image is different. Hence, if you let the neighborhood-based method use a large window to significantly suppress noise, it cannot preserve the detail information such as the edge of a changed area. To overcome this drawback, we propose a spatial-temporal adaptive neighborhood-based ratio (STANR) approach for change detection in SAR images. STANR employs heterogeneity to adaptively select the spatial homogeneity neighborhood and uses the temporal adaptive strategy to determine multi-temporal neighborhood windows. Experimental results on two data sets show that STANR can both suppress the negative influence of noise and preserve edge details, and can obtain a better difference image than other state-of-the-art methods.

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.

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.

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 Optimization of Classified Satellite Images using DWT and Fuzzy Logic

2013 ◽  
Vol 2 (02) ◽  
pp. 08-12
Author(s):  
Mr. Kaustubh Patil
Keyword(s):  
Satellite Images ◽  
Clustering Algorithm ◽  
Speckle Noise ◽  
Intermediate Stage ◽  
Sar Images ◽  
Fusion Strategy ◽  
Difference Image ◽  
Fuzzy Clustering Algorithm ◽  
High Level ◽  
Fuzzy C Means Algorithm

The image taken by a satellite can be enhanced in terms of its resolution based on the interpolation can be obtained by DWT. Using DWT, the image at the input is divided into several sub bands and the speckle noise is also removed. Thereafter, the high-level images and low-level image at the input can be combined, to produce a better image applying IDWT. An intermediate stage for approximating high level is proposed here. The variation in detection approaches for SAR images are done by using image fusion strategy and novel fuzzy clustering algorithm. To retrieve an enhanced image, wavelet fusion directives are considered to combine the wavelet coefficients. A fuzzy C-means algorithm is proposed for identifying the altered and unaltered regions in the combined difference image.

scholarly journals Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images

2019 ◽  
Vol 11 (2) ◽  
pp. 142 ◽  
Author(s):  
Wenping Ma ◽  
Hui Yang ◽  
Yue Wu ◽  
Yunta Xiong ◽  
Tao Hu ◽  
...  
Keyword(s):  
Change Detection ◽  
Representation Learning ◽  
Local Information ◽  
Learning Ability ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Image Block ◽  
Sar Images ◽  
Difference Image ◽  
Multi Scale

In this paper, a novel change detection approach based on multi-grained cascade forest(gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detectsthe changed and unchanged areas of the images by using the well-trained gcForest. Most existingchange detection methods need to select the appropriate size of the image block. However, thesingle size image block only provides a part of the local information, and gcForest cannot achieve agood effect on the image representation learning ability. Therefore, the proposed approach choosesdifferent sizes of image blocks as the input of gcForest, which can learn more image characteristicsand reduce the influence of the local information of the image on the classification result as well.In addition, in order to improve the detection accuracy of those pixels whose gray value changesabruptly, the proposed approach combines gradient information of the difference image with theprobability map obtained from the well-trained gcForest. Therefore, the image edge information canbe enhanced and the accuracy of edge detection can be improved by extracting the image gradientinformation. Experiments on four data sets indicate that the proposed approach outperforms otherstate-of-the-art algorithms.

scholarly journals Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network

2019 ◽  
Vol 11 (9) ◽  
pp. 1091 ◽  
Author(s):  
Lu Li ◽  
Chao Wang ◽  
Hong Zhang ◽  
Bo Zhang ◽  
Fan Wu
Keyword(s):  
Change Detection ◽  
Rapid Development ◽  
Ratio Method ◽  
Small Scale ◽  
City Management ◽  
Spatiotemporal Resolution ◽  
Sar Images ◽  
Difference Image ◽  
Radar Images ◽  
Log Ratio

With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method.

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 Performance Evaluation of Change Detection in SAR Images Based on Hybrid Antlion DWT Fuzzy c-Means Clustering

2021 ◽  
Vol 21 (2) ◽  
pp. 45-57
Author(s):  
J. Thrisul Kumar ◽  
B. M. S. Rani ◽  
M. Satish Kumar ◽  
M. V. Raju ◽  
K. Maria Das
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Geographical Area ◽  
Ground Truth ◽  
Sar Images ◽  
Fuzzy C Means ◽  
Difference Image ◽  
Multi Temporal ◽  
Fuzzy C Means Clustering ◽  
Log Ratio

Abstract In this paper, the main objective is to detect changes in the geographical area of Ottawa city in Canada due to floods. Two multi-temporal Synthetic Aperture Radar (SAR) images have been taken to evaluate the un-supervised change detection process. In this process, two ratio operators named as Log-Ratio and Mean-Ratio are used to generate a difference image. Performing image fusion based on DWT by selecting optimum filter coefficients by satisfying the wavelet filter coefficient properties through a novel image fusion technique is named as ADWT. GA, PSO, AntLion Optimization algorithms (ALO) and Hybridized AntLion Algorithm (HALO) have been adapted to perform the ADWT based image fusion. Segmentation has been performed based on fuzzy c-Means clustering to detect changed and unchanged pixels. Finally, the performance of the proposed method will be analysed by comparing the segmented image with the ground truth image in terms of sensitivity, accuracy, specificity, precision, F1-score.

scholarly journals Change Target Extraction Based on Scale-Adaptive Difference Image and Morphology Filter for KOMPSAT-5

2022 ◽  
Vol 14 (2) ◽  
pp. 245
Author(s):  
Yeonju Choi ◽  
Dochul Yang ◽  
Sanghyuck Han ◽  
Jaeung Han
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Weather Conditions ◽  
Extraction Process ◽  
Detection Accuracy ◽  
Sar Images ◽  
Difference Image ◽  
Proposed Model ◽  
Target Extraction ◽  
Filtering Technique

Multitemporal synthetic aperture radar (SAR) images have been widely used for change detection and monitoring of the environment owing to their competency under all weather conditions. However, owing to speckle backgrounds and strong reflections, change detection in urban areas is challenging. In this study, to automatically extract changed objects, we developed a model that integrated change detection and object extraction in multiple Korean Multi-Purpose Satellite-5 (KOMPSAT-5) images. Initially, two arbitrary L1A-level SAR images were input into the proposed model, and after pre-processing, such as radio calibration and coordinate system processing, change detection was performed. Subsequently, the desired targets were automatically extracted from the change detection results. Finally, the model obtained images of the extraction targets and metadata, such as date and location. Noise was removed by applying scale-adaptive modification to the generated difference image during the change detection process, and the detection accuracy was improved by emphasizing the occurrence of the change. After polygonizing the pixel groups of the change detection map in the target extraction process, the morphology-based object filtering technique was applied to minimize the false detection rate. As a result of the proposed approach, the changed objects in the KOMPSAT-5 images were automatically extracted with 90% accuracy.

AN APPROACH FOR IMPROVING ACCURACY OF CHANGE DETECTION IN MULTI-TEMOPRAL SAR IMAGES

2020 ◽  
pp. 47-54
Author(s):  
Nguyen Hung An
Keyword(s):  
Change Detection ◽  
Natural Resource ◽  
Speckle Noise ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Sar Images ◽  
Novel Approach ◽  
Multi Temporal ◽  
Improving Accuracy

Algorithms of change detection in multi-temporal SAR images have received great interests for recent decades, and been widely applied in natural resource supervision activities. However, these algorithms still expose the limitation of detection accuracy due to inhenrent presence of speckle noise in SAR images. This paper developed a novel approach of change detection in multi-temporal SAR images of sea surface. The algorithm has increased accuracy of change detection in multi-temporal SAR images of sea surface compared with recent other methods.

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