MULTIRESOLUTION BASED TEXTURAL ANALYSIS OF REMOTELY SENSED IMAGES FOR CHANGE DETECTION

机译：基于多分辨率的遥感图像变化检测纹理分析

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This paper presents a multiresolution textural approach to change detection in multi-temporal synthetic aperture radar (SAR) images. Texture analysis is often discussed in image processing domain, but most methods do not exploit the fact that texture occurs at various spatial scales. Often used techniques such as the gray level co-occurrence statistics is limited to altering inter-neighbor spacing and hence does not capture the texture very well. The proposed approach exploits curvelet and contourlet based multi-scale decomposition of Pauli RGB decomposed images from SAR data where textural information is extracted at various scales and in different directions in terms of statistical moments and energy to generate the feature map. The L1-norm is used in the proposed method to generate the difference image, which is thresholded using the maximum entropy principle to obtain final change detection map. The results are compared with the changes detected by wavelet based textural features. Accuracy assessment is performed for change maps and comparative analysis is carried out in terms of missed changes, false-alarms and overall accuracies. It is found that the proposed method exhibits high change detection accuracy with better edge continuity.

机译：本文提出了一种用于多时相合成孔径雷达（SAR）图像变化检测的多分辨率纹理方法。纹理分析经常在图像处理领域中讨论，但是大多数方法都没有利用纹理发生在各种空间尺度上这一事实。通常使用的技术（例如，灰度级共现统计信息）仅限于更改邻居之间的间隔，因此不能很好地捕获纹理。所提出的方法利用基于SAR数据的Pauli RGB分解图像的基于Curvelet和Contourlet的多尺度分解，其中从统计尺度和能量方面以不同比例和不同方向提取纹理信息，以生成特征图。所提出的方法使用L1-范数生成差分图像，该差分图像使用最大熵原理进行阈值确定以获得最终变化检测图。将结果与基于小波的纹理特征检测到的变化进行比较。对变更图进行准确性评估，并根据遗漏的变更，错误警报和整体准确性进行比较分析。发现该方法具有较高的变化检测精度和较好的边缘连续性。

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《Asian conference on remote sensing;ACRS》|2017年|2161-2165|共5页
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Rizwan Ahmed Ansari; Krishna Mohan Buddhiraju; Avik Bhattacharya;
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change detection; texture; Pauli RGB; wavelet; curvelet; contourlet;

机译：变更检测;质地;泡利RGB;小波曲线轮廓波;

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1. Textural segmentation of remotely sensed images using multiresolution analysis for slum area identification [J] . Rizwan Ahmed Ansari, Krishna Mohan Buddhiraju European Journal of Remote Sensing . 2019,第sup2期

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2. Building change detection from remotely sensed images based on spatial domain analysis and Markov random field [J] . Zong Kaibin, Sowmya Arcot, Trinder John Journal of Applied Remote Sensing . 2019,第2期

机译：基于空间域分析和马尔可夫随机字段，从远程感测图像构建变更检测
3. Change detection from remotely sensed images: From pixel-based to object-based approaches [J] . Masroor Hussain, Dongmei Chen, Angela Cheng, ISPRS Journal of Photogrammetry and Remote Sensing . 2013,第juna期

机译：遥感图像的变化检测：从基于像素的方法到基于对象的方法
4. MULTIRESOLUTION BASED TEXTURAL ANALYSIS OF REMOTELY SENSED IMAGES FOR CHANGE DETECTION [C] . Rizwan Ahmed Ansari, Krishna Mohan Buddhiraju, Avik Bhattacharya Asian conference on remote sensingACRS . 2017

机译：基于多分辨率的变化图像的纹理分析
5. Unsupervised change detection in remotely sensed images. [D] . Rejaaishushtari, Seyed Ali. 2001

机译：遥感图像中的无监督变更检测。
6. Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application [O] . Susan K. Maxwell -1

机译：使用基于对象的图像分析从远程感测数据生成陆地覆盖边界：概述和流行病学应用
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MULTIRESOLUTION BASED TEXTURAL ANALYSIS OF REMOTELY SENSED IMAGES FOR CHANGE DETECTION

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