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 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.

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 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.

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 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 Modified S2CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection

2018 ◽  
Vol 10 (9) ◽  
pp. 3301 ◽  
Author(s):  
Honglyun Park ◽  
Jaewan Choi ◽  
Wanyong Park ◽  
Hyunchun Park
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Seasonal Effects ◽  
Detection Accuracy ◽  
Difference Image ◽  
Before And After ◽  
Change Detection Algorithms ◽  
The Difference ◽  
Direction Information

This study aims to reduce the false alarm rate due to relief displacement and seasonal effects of high-spatial-resolution multitemporal satellite images in change detection algorithms. Cross-sharpened images were used to increase the accuracy of unsupervised change detection results. A cross-sharpened image is defined as a combination of synthetically pan-sharpened images obtained from the pan-sharpening of multitemporal images (two panchromatic and two multispectral images) acquired before and after the change. A total of four cross-sharpened images were generated and used in combination for change detection. Sequential spectral change vector analysis (S2CVA), which comprises the magnitude and direction information of the difference image of the multitemporal images, was applied to minimize the false alarm rate using cross-sharpened images. Specifically, the direction information of S2CVA was used to minimize the false alarm rate when applying S2CVA algorithms to cross-sharpened images. We improved the change detection accuracy by integrating the magnitude and direction information obtained using S2CVA for the cross-sharpened images. In the experiment using KOMPSAT-2 satellite imagery, the false alarm rate of the change detection results decreased with the use of cross-sharpened images compared to that with the use of only the magnitude information from the original S2CVA.

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 Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 1987 ◽  
Author(s):  
Rocío Ramos-Bernal ◽  
René Vázquez-Jiménez ◽  
Raúl Romero-Calcerrada ◽  
Patricia Arrogante-Funes ◽  
Carlos Novillo
Keyword(s):  
Change Detection ◽  
Vegetation Index ◽  
Thermal Emission ◽  
Principal Component ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Types ◽  
Statistical Parameters ◽  
Wide Range ◽  
The World

Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.

scholarly journals A supervised method for object-based 3D building change detection on aerial stereo images

2014 ◽  
Vol XL-3 ◽  
pp. 259-264 ◽  
Author(s):  
R. Qin ◽  
A. Gruen
Keyword(s):  
Change Detection ◽  
Current Trend ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Status Change ◽  
Aerial Photos ◽  
Object Based ◽  
The Status

There is a great demand for studying the changes of buildings over time. The current trend for building change detection combines the orthophoto and DSM (Digital Surface Models). The pixel-based change detection methods are very sensitive to the quality of the images and DSMs, while the object-based methods are more robust towards these problems. In this paper, we propose a supervised method for building change detection. After a segment-based SVM (Support Vector Machine) classification with features extracted from the orthophoto and DSM, we focus on the detection of the building changes of different periods by measuring their height and texture differences, as well as their shapes. A decision tree analysis is used to assess the probability of change for each building segment and the traffic lighting system is used to indicate the status "change", "non-change" and "uncertain change" for building segments. The proposed method is applied to scanned aerial photos of the city of Zurich in 2002 and 2007, and the results have demonstrated that our method is able to achieve high detection accuracy.

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.

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