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

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.

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

Remote Sensing ◽

10.3390/rs11020142 ◽

2019 ◽

Vol 11 (2) ◽

pp. 142 ◽

Cited By ~ 10

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.

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

Remote Sensing ◽

10.3390/rs10081295 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1295 ◽

Cited By ~ 5

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.

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A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images

Remote Sensing ◽

10.3390/rs12111746 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1746

Author(s):

Salman Ahmadi ◽

Saeid Homayouni

Keyword(s):

Synthetic Aperture Radar ◽

Change Detection ◽

Active Contours ◽

Training Data ◽

Synthetic Aperture ◽

Data Sets ◽

Difference Image ◽

Proposed Model ◽

The Difference ◽

Aperture Radar

In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods.

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BUILDING CHANGE DETECTION FROM BITEMPORAL AERIAL IMAGES USING DEEP LEARNING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-565-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 565-571

Author(s):

S. Su ◽

T. Nawata ◽

T. Fuse

Keyword(s):

Deep Learning ◽

Change Detection ◽

Urban Areas ◽

Network Architecture ◽

Aerial Images ◽

Detection Accuracy ◽

Detection Model ◽

Wide Range ◽

Surface Models ◽

Data Source

Abstract. Automatic building change detection has become a topical issue owing to its wide range of applications, such as updating building maps. However, accurate building change detection remains challenging, particularly in urban areas. Thus far, there has been limited research on the use of the outdated building map (the building map before the update, referred to herein as the old-map) to increase the accuracy of building change detection. This paper presents a novel deep-learning-based method for building change detection using bitemporal aerial images containing RGB bands, bitemporal digital surface models (DSMs), and an old-map. The aerial images have two types of spatial resolutions, 12.5 cm or 16 cm, and the cell size of the DSMs is 50 cm × 50 cm. The bitemporal aerial images, the height variations calculated using the differences between the bitemporal DSMs, and the old-map were fed into a network architecture to build an automatic building change detection model. The performance of the model was quantitatively and qualitatively evaluated for an urban area that covered approximately 10 km2 and contained over 21,000 buildings. The results indicate that it can detect the building changes with optimum accuracy as compared to other methods that use inputs such as i) bitemporal aerial images only, ii) bitemporal aerial images and bitemporal DSMs, and iii) bitemporal aerial images and an old-map. The proposed method achieved recall rates of 89.3%, 88.8%, and 99.5% for new, demolished, and other buildings, respectively. The results also demonstrate that the old-map is an effective data source for increasing building change detection accuracy.

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

Sustainability ◽

10.3390/su10093301 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3301 ◽

Cited By ~ 3

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.

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

Remote Sensing ◽

10.3390/rs11091091 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1091 ◽

Cited By ~ 10

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.

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Markovian Change Detection of Urban Areas Using Very High Resolution Complex SAR Images

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2013.2284297 ◽

2014 ◽

Vol 11 (5) ◽

pp. 995-999 ◽

Cited By ~ 31

Author(s):

Fabio Baselice ◽

Giampaolo Ferraioli ◽

Vito Pascazio

Keyword(s):

High Resolution ◽

Change Detection ◽

Urban Areas ◽

Sar Images ◽

Very High

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

Cybernetics and Information Technologies ◽

10.2478/cait-2021-0018 ◽

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

Remote Sensing ◽

10.3390/rs13183697 ◽

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.

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Change Analysis in Urban Areas Based on Statistical Features and Temporal Clustering Using TerraSAR-X Time-Series Images

Remote Sensing ◽

10.3390/rs11080926 ◽

2019 ◽

Vol 11 (8) ◽

pp. 926 ◽

Cited By ~ 2

Author(s):

Jili Yuan ◽

Xiaolei Lv ◽

Fangjia Dou ◽

Jingchuan Yao

Keyword(s):

Time Series ◽

Change Detection ◽

Urban Areas ◽

High Efficiency ◽

Temporal Change ◽

Cluster Number ◽

Change Analysis ◽

Sar Images ◽

Temporal Dimension ◽

Temporal Grouping

The existing unsupervised multitemporal change detection approaches for synthetic aperture radar (SAR) images based on the pixel level usually suffer from the serious influence of speckle noise, and the classification accuracy of temporal change patterns is liable to be affected by the generation method of similarity matrices and the pre-specified cluster number. To address these issues, a novel time-series change detection method with high efficiency is proposed in this paper. Firstly, spatial feature extraction using local statistical information on patches is conducted to reduce the noise and for subsequent temporal grouping. Secondly, a density-based clustering method is adopted to categorize the pixel series in the temporal dimension, in view of its efficiency and robustness. Change detection and classification results are then obtained by a fast differential strategy in the final step. The experimental results and analysis of synthetic and realistic time-series SAR images acquired by TerraSAR-X in urban areas demonstrate the effectiveness of the proposed method, which outperforms other approaches in terms of both qualitative results and quantitative indices of macro F1-scores and micro F1-scores. Furthermore, we make the case that more temporal change information for buildings can be obtained, which includes when the first and last detected change occurred and the frequency of changes.

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