scholarly journals Extraction of farmland areas on different row directions with SAR image based on texture feature

2013 ◽  
Author(s):  
Ren Jian-hua ◽  
Zhao Kai ◽  
Zheng Xing-ming ◽  
Li Yang-yang
Keyword(s):  
Texture Feature ◽  
Sar Image

An efficient method of SAR image segmentation based on texture feature

2017 ◽  
Vol 16 (4) ◽  
pp. 855-864 ◽  
Author(s):  
Xiaorong Xue ◽  
Jipeng Wang ◽  
Fang Xiang ◽  
Hongfu Wang
Keyword(s):  
Image Segmentation ◽  
Efficient Method ◽  
Texture Feature ◽  
Sar Image ◽  
Sar Image Segmentation

Building Damage Information Extraction From Fully Polarimetric SAR Images Based on Variogram Texture Feature

2020 ◽  
Author(s):  
Wei Zhai ◽  
Xiu-lai Xiao ◽  
Hao-ran Zhang
Keyword(s):  
Texture Feature ◽  
Google Earth ◽  
Optical Remote Sensing ◽  
Sar Image ◽  
Threshold Values ◽  
Polarimetric Sar ◽  
Texture Information ◽  
Building Area ◽  
Collapsed Buildings ◽  
Collapsed Building

<p>Rapid evaluation of building earthquake disaster information is of great significance for earthquake emergency rescue. Although polarimetric SAR has rich polarimetric information, there are still clear texture information in polarimetric SAR that could not be ignored, especially the intact artificial buildings show regular texture features in the image, and the texture distribution in the collapsed building area is disordered, so combining the texture information can also extract the building information well. In this paper, the full polarization SAR data of Yushu area in 2010 is taken as the research object, and the building area in SAR image is extracted by using the volume scattering component P<sub>V</sub> in Yamaguchi decomposition. On this basis, the intact building area and collapsed building area are extracted based on the variogram value. Comparing and analyzing the result with the intact building area is extracted by using the secondary scattering component P<sub>D</sub> in Yamaguchi decomposition. Finally, verified the accuracy by combing the optical remote sensing image after the earthquake, the extraction accuracy of intact buildings is 80.18%, collapsed buildings is 84.54%, and road water system is 77.58%.</p><p>Firstly, buildings and non-buildings are distinguished in SAR image. 100 sample matrixes are selected in building area and non-building area on P<sub>V</sub> component image respectively. After calculating the mean value of sample matrixes, the threshold values of building and non-building area are obtained based on the minimum error, and the building area and non-building area are extracted respectively according to the threshold values. Secondly, in the building area, the sample matrix of intact buildings and collapsed buildings is selected to calculate the variograms value, and then the variograms curve is drawn. When the range a = 11, the variograms value of the building area is calculated, and the FCM algorithm is used to extract the calculation results of intact buildings and collapsed buildings respectively; In order to compare and analyze the classification results, based on P<sub>D </sub>component, use K-means algorithm to extract intact buildings and the collapsed building areas are extracted separately, and the results are compared with the results based on the variogram texture feature method. Finally, the intact buildings and collapsed buildings extracted are calibrated and the extraction accuracy is calculated by combining the Google Earth historical image.</p><p>At the end of this paper, the shortcomings of extraction results based on Yamaguchi four component decomposition method and variogram method are discussed, and the idea of combining geographic information data to further improve the accuracy of earthquake damage assessment is proposed.</p>

scholarly journals BUILDING EXTRACTION OF POLARIMETRIC SAR IMAGE BASED ON ASSOCIATED FEATURE AND SVM

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 587-592
Author(s):  
Y. Sheng ◽  
Z. Zhao ◽  
H. Chen
Keyword(s):  
Urban Areas ◽  
Extraction Method ◽  
Environmental Planning ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Sar Image ◽  
Building Extraction ◽  
Novel Method ◽  
Urban Environmental Planning

Abstract. The identification and extraction of building is a vital task in urban environmental planning and research. High-resolution SAR technology has the advantages of all-day, all-weather and strong penetration, and has become one of the important technical means to study urban areas. A novel method for building extraction using both associated features and SVM that based on the full polarization SAR image of GF-3 is proposed in this paper. Firstly, filtering the GF-3 image to reduce the speckles of the image, then texture features based on Span map are extracted by using GLCM. Principal component analysis is used to remove the correlation between them and select the best texture features. The normalized circular-pol correlation coefficient is introduced as the polarization feature and combines with the best texture features. Finally, the image is classified and extracted by SVM. In this paper, the extraction results are compared with the results of the texture feature building extraction method. The experimental results show that the proposed method can obtain higher extraction precision, and the extraction effect is obviously optimized.

scholarly journals Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features

2016 ◽  
Author(s):  
Qiang Li ◽  
Lixia Gong ◽  
Jingfa Zhang
Keyword(s):  
Change Detection ◽  
Texture Feature ◽  
Texture Features ◽  
Seismic Damage ◽  
Segmentation Algorithm ◽  
Image Texture ◽  
Sar Image ◽  
Watershed Segmentation ◽  
Sar Images ◽  
Feature Parameters

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

scholarly journals Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification

2021 ◽  
Vol 11 (4) ◽  
pp. 1603
Author(s):  
Xiaoying Wu ◽  
Xianbin Wen ◽  
Haixia Xu ◽  
Liming Yuan ◽  
Changlun Guo
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Feature Fusion ◽  
Texture Feature ◽  
Sar Image ◽  
Local Consistency ◽  
Sensing Applications ◽  
Adaptive Kernel ◽  
Feature Information ◽  
Kernel Combination

Synthetic aperture radar (SAR) image classification is an important task in remote sensing applications. However, it is challenging due to the speckle embedding in SAR imaging, which significantly degrades the classification performance. To address this issue, a new SAR image classification framework based on multi-feature fusion and adaptive kernel combination is proposed in this paper. Expressing pixel similarity by non-negative logarithmic likelihood difference, the generalized neighborhoods are newly defined. The adaptive kernel combination is designed on them to dynamically explore multi-feature information that is robust to speckle noise. Then, local consistency optimization is further applied to enhance label spatial smoothness during classification. By simultaneously utilizing adaptive kernel combination and local consistency optimization for the first time, the texture feature information, context information within features, generalized spatial information between features, and complementary information among features is fully integrated to ensure accurate and smooth classification. Compared with several state-of-the-art methods on synthetic and real SAR images, the proposed method demonstrates better performance in visual effects and classification quality, as the image edges and details are better preserved according to the experimental results.

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