scholarly journals Automatic Identification of the Number and Values of Decision Thresholds in the Log-Ratio Image for Change Detection in SAR Images

2006 ◽  
Vol 3 (3) ◽  
pp. 349-353 ◽  
Author(s):  
Y. Bazi ◽  
L. Bruzzone ◽  
F. Melgani
Keyword(s):  
Change Detection ◽  
Automatic Identification ◽  
Sar Images ◽  
Ratio Image ◽  
Log Ratio

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 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 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 Change Detection in Water reservoir using Image Fusion and Supervised Classifier

2021 ◽  
pp. 324-330
Author(s):  
Kiran Khandarkar ◽  
Dr. Sharvari Tamne
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Classification System ◽  
Supervised Classification ◽  
Water Reservoir ◽  
Input Image ◽  
Sar Images ◽  
The Mean ◽  
Log Ratio

The research provides a method for improving change detection in SAR images using a fusion object and a supervised classification system. To remove noise from the input image, we use the DnCNN denoising approach. The data from the first image is then processed with the mean ratio operator. The log ratio operator is used to process the second image. These two images are fused together using SWT-based image fusion, and the output is sent to a supervise classifier for change detection.

scholarly journals It is a misunderstanding that log ratio outperforms ratio in change detection of SAR images

2019 ◽  
Vol 52 (1) ◽  
pp. 484-492
Author(s):  
Huifu Zhuang ◽  
Zhixiang Tan ◽  
Kazhong Deng ◽  
Hongdong Fan
Keyword(s):  
Change Detection ◽  
Sar Images ◽  
Log Ratio

scholarly journals Small-Target Detection between SAR Images Based on Statistical Modeling of Log-Ratio Operator

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1431
Author(s):  
Chao Chen ◽  
Kuihua Huang ◽  
Gui Gao
Keyword(s):  
Change Detection ◽  
Target Detection ◽  
Small Target ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Ml Estimation ◽  
Small Target Detection ◽  
Multi Temporal ◽  
Lr Operator ◽  
Log Ratio

The log-ratio (LR) operator is well suited for change detection in synthetic aperture radar (SAR) amplitude or intensity images. In applying the LR operator to change detection in multi-temporal SAR images, a crucial problem is how to develop precise models for the LR statistics. In this study, we first derive analytically the probability density function (PDF) of the LR operator. Subsequently, the PDF of the LR statistics is parameterized by three parameters, i.e., the number of looks, the coherence magnitude, and the true intensity ratio. Then, the maximum-likelihood (ML) estimates of parameters in the LR PDF are also derived. As an example, the proposed statistical model and corresponding ML estimation are used in an operational application, i.e., determining the constant false alarm rate (CFAR) detection thresholds for small target detection between SAR images. The effectiveness of the proposed model and corresponding ML estimation are verified by applying them to measured multi-temporal SAR images, and comparing the results to the well-known generalized Gaussian (GG) distribution; the usefulness of the proposed LR PDF for small target detection is also shown.

scholarly journals Change Detection in SAR Images using Image Fusion and Supervised Classifier

2020 ◽  
Vol 9 (1) ◽  
pp. 685-687
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Classification System ◽  
Supervised Classification ◽  
Input Image ◽  
Sar Images ◽  
Denoising Method ◽  
Pca Algorithm ◽  
Log Ratio

The paper proposes an approach based on a fusion o bject and a supervised classification system to improve detection f or SAR images. Here we are using CNN denoising method for removing noise in the input image. Then information from first image is processed using mean_ratio operator. Second image is processed by log ratio operator. These two images are fused using PCA algorithm and the output is provided to KNN supervised classifier for finding change detection in the image.

scholarly journals A SAR INTENSITY IMAGES CHANGE DETECTION METHOD BASED ON FUSION DIFFERENCE DETECTOR AND STATISTICAL PROPERTIES

2017 ◽  
Vol IV-2/W4 ◽  
pp. 439-443 ◽  
Author(s):  
B. Cui ◽  
Y. Zhang ◽  
L. Yan ◽  
X. Cai
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Hypothesis Test ◽  
Threshold Selection ◽  
Sar Images ◽  
Difference Map ◽  
Multi Temporal ◽  
Intensity Images ◽  
Log Ratio ◽  
Change Threshold

Detecting the land cover changes is an important application of multi-temporal synthetic aperture radar (SAR) images. This study puts forward a novel SAR change detection method which has two-steps: change detector construction and change threshold selection. For change detector construction, considering the SAR intensity images follow the gamma distribution, the conditional probabilities of the binary hypothesis test are provided, then the log likelihood ratio (LLR) combined with the log ratio (LR) to construct a detector which can enhance the degree of change to calculate the diversity degree convenient between the two images; for change threshold selection, owing to the characteristic that the curve about the ratio value of adjacent grey-level (GL) values in normalized difference map, the normalized difference map can be segmented in three parts by two thresholds selected which correspond to the regions of unchanged, backscatter enhanced and weakened separately. And as this, the change areas can be also determined simultaneously. The experimental results on different areas and sensors indicate that the proposed algorithm is effective and feasible.

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