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2nd International Conference on Mining Engineering and Metallurgical Technology (MEMT)

作者： Lian Lian Chen Jianfei Guangzhou Univ Sch Geog Sci Guangzhou 510006 Guangdong Peoples R China

To verify a method of choosing the optimal segmentation scale, the Guangzhou Higher Education Mega Center is selected as test area for surface features extraction. Buildings, roads, waters and vegetations are extracted from 4 different resolution images using different scales. It is found that the optimal segmentation scale selected based on standard deviation of the means and mean of standard deviations of image objects' brightness is almost consistent with the optimal segmentation scale selected based on actual classification. The classification accuracy is related with spatial resolution. But for specific applications, higher resolution doesn't definitely get better classification accuracy. (C) 2011 Published by Elsevier Ltd.

关键词： Remote sensing image interpretation object-oriented method segmentation scale optimal scale classification accuracy

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Research on segmentation scale of Multi-Resources Remote Sensing Data Based on Object-Oriented

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Procedia Earth and Planetary Science 2011年 2卷 352-357页

作者： Lian Lian Jianfei Chen School of Geographical Science Guangzhou University Guangzhou 510006 China

To verify a method of choosing the optimal segmentation scale, the Guangzhou Higher Education Mega Center is selected as test area for surface features extraction. Buildings, roads, waters and vegetations are extracted from 4 different resolution images using different scales. It is found that the optimal segmentation scale selected based on standard deviation of the means and mean of standard deviations of image objects’ brightness is almost consistent with the optimal segmentation scale selected based on actual classification. The classification accuracy is related with spatial resolution. But for specific applications, higher resolution doesn’t definitely get better classification accuracy.

关键词： Remote sensing image interpretation object-oriented method segmentation scale optimal scale classification accuracy

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scale SELECTION BASED ON MORAN'S I FOR segmentation OF HIGH RESOLUTION REMOTELY SENSED IMAGES

SCALE SELECTION BASED ON MORAN'S I FOR SEGMENTATION OF HIGH ...

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IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

作者： Meng, Yan Lin, Chao Cuiand, Weihong Yao, Jian Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781479957750

Image segmentation is a prerequisite for object-based image analysis (OBIA). However, selecting an optimal segmentation scale is often time consuming and needs trial-and-error. This paper presents an unsupervised scale selection method based on the rate of change of a spatial autocorrelation indicator - the global Moran's I for segmentation of high resolution remotely sensed images. It was compared with other two scale selection methods and its effectiveness is validated through both visual analysis and by referencing to multiple manual segmentations. Experimental results on our own data and statistical data from an external reference showed that the optimal scale could be easily selected through the proposed method.

关键词： Local Variance Spatial Autocorrelation segmentation scale High Resolution Moran's I

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scale SELECTION BASED ON MORAN'S I FOR segmentation OF HIGH RESOLUTION REMOTELY SENSED IMAGES

SCALE SELECTION BASED ON MORAN'S I FOR SEGMENTATION OF HIGH ...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Yan Meng Chao Lin Weihong Cui Jian Yao School of Remote Sensing and Information Engineering Wuhan University Beijing GEOWAY Software Co. Ltd

ISBN: (纸本)9781479953141

关键词： Local Variance Spatial Autocorrelation segmentation scale High Resolution Moran's I

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Object-specific optimization of hierarchical multiscale segmentations for high-spatial resolution remote sensing images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2020年 159卷 308-321页

作者： Zhang, Xueliang Xiao, Pengfeng Feng, Xuezhi Nanjing Univ Sch Geog & Ocean Sci Dept Geog Informat Sci Nanjing 210023 Jiangsu Peoples R China Collaborat Innovat Ctr South China Sea Studies Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China

Accurate segmentation of high-spatial resolution remote sensing images remains a challenging problem for geographic object-based image analysis. An object-specific optimization method for hierarchical multiscale segmentations is proposed in this study by fusing multiple segmentations into an optimized segmentation with specific consideration of each object. Based on a segment tree model representing hierarchical multiscale segmentations, the framework of object-specific optimization is achieved by identifying and fusing the meaningful nodes in each path originating from a leaf node. Within the optimization framework, an optimization measure for identifying meaningful node is designed according to the maximum change of homogeneity in a path. The proposed optimization method is experimentally validated to hold the advantage of improving segmentation accuracy by the manner of object-specific optimization as well as the potential of automatically producing optimized segmentation for successive object-based analysis.

关键词： Image segmentation segmentation scale Hierarchical multiscale segmentation Region merging Geographic object-based image analysis

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Extraction of Change Information based on Multi-sequence Image Objects

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EUROPEAN JOURNAL OF REMOTE SENSING 2016年第1期49卷 465-487页

作者： Zhang, Haitao Cheng, Xinwen China Univ Geosci Dept Informat Engn 388 Lumo Rd Wuhan 430074 Peoples R China

For identification of change information, most studies focus on pixel-based techniques for low-resolution images, while few studies have examined object-based techniques for high-resolution images. Moreover, most of the techniques are complex and have a high requirement for the segmentation scale. This paper proposes a change detection method based on multi-sequence image objects and introduces the use of arithmetic progression to generate the set of segmentation scales. Pre-event and post-event images are segmented with multi-scales, respectively, and sub-objects are obtained based on the division of the minimum segmentation scales of bi-temporal images. Change feature vectors are constructed for each associated object of sub-object and vectors' magnitude is computed. After the determination of change threshold values, the change feature vectors are used to confirm whether sub-objects have changed, providing final change information. This method was tested using the bi-temporal World View 2 images taken before and after a landslide. The results confirm the feasibility of the method presented in this paper, and show its high accuracy through a comparison with the changing vector analysis method and the post-classification comparison method based on object-oriented theory. The approach outlined herein would be helpful for extraction of change information in high-resolution images.

关键词： Change detection arithmetic progression segmentation scale sub-objects feature vector

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Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing

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EUROPEAN JOURNAL OF REMOTE SENSING 2022年第1期55卷 485-506页

作者： Guan, Qiang Xu, Tongyu Feng, Shuai Yu, Fenghua Song, Kai Shenyang Agr Univ Coll Informat & Elect Engn Shenyang Liaoning Peoples R China Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao Inner Mongolia Peoples R China Liaoning Engn Res Ctr Informat Technol Agr Shenyang Liaoning Peoples R China Shenyang Ligong Univ Sch Informat Sci & Engn Shenyang Liaoning Peoples R China

Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability's influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of endmembers and fully constrained least squares based on distance strategy (D-FCLS) to improve the efficiency of abundance estimation. In the segmentation-scale determination method, this study establishes a segmentation scale division criterion to divide segmented images with similar quality into the same segmentation scale. The optimal segmentation scale is selected according to the actual situation of hyperspectral images. Moreover, the distance strategy is applied to fully constrained least squares (FCLS) using the spatial relationship between endmembers and the mixed pixel in abundance estimation. The proposed methods are evaluated on the synthetic and real datasets. The results show that the validity of the segmentation-scale determination method is verified by quantitative and qualitative evaluation on all datasets. In terms of abundance estimation, compared with FCLS, D-FCLS improves the efficiency by more than 10.30% on the synthetic dataset and 18.71% on the real dataset. In addition, this study's proposed abundance estimation method and unsupervised superpixel-based HU method are superior to the other comparison methods.

关键词： Hyperspectral unmixing superpixel segmentation segmentation scale abundance estimation fully constrained least squares distance strategy

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A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年 49卷 87-98页

作者： Li, Manchun Ma, Lei Blaschke, Thomas Cheng, Liang Tiede, Dirk Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing 210023 Jiangsu Peoples R China Salzburg Univ Dept Geoinformat Z GIS Hellbrunner Str 34 A-5020 Salzburg Austria

Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping. (C) 2016 Elsevier B.V. All rights reserved.

关键词： GEOBIA OBIA Random Forest segmentation scale Training set size Feature selection Mixed object Classification High spatial resolution

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Land cover changed object detection in remote sensing data with medium spatial resolution

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2015年 38卷 129-137页

作者： Yang, Xiao Tong Liu, Huiping Gao, Xiaofeng State Key Lab Remote Sensing Sci Beijing Peoples R China Beijing Key Lab Environm Remote Sensing & Digital Beijing Peoples R China Beijing Normal Univ Sch Geog Beijing 100875 Peoples R China

Land cover change information is crucial to analyse the process and the change patterns of environments and ecological systems. Recent studies have incorporated object-based image analysis for its ability to generate meaningful geographical objects into studies of change detection. In this research, we developed a systematic methodology to realise multi-type land cover changed object detection with medium spatial resolution remote sensing images in Beijing, China. Optimum index factor (OIF) was applied to determine the best change indicators and the chi-square transformation was carried out to determine the change threshold of the 4 classes of changed object. The clustering change vectors in the feature space were proposed to discriminate the change types. According to the accuracy assessment, the overall accuracy of changed/unchanged object detection was approximately 93.9% with an overall kappa of 0.824, and the change type discrimination also achieved an overall accuracy of 81.67%, indicating the effectiveness of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Multi-temporal segmentation Land cover changed object Change indicators segmentation scale Chi-square transformation

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NEW DETECTOR BASED ON PATCH segmentation FOR HIGH RESOLUTION SAR IMAGE

NEW DETECTOR BASED ON PATCH SEGMENTATION FOR HIGH RESOLUTION...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhang, Bo Wang, Chao Wu, Fan Zhang, Hong Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100094 Peoples R China

ISBN: (纸本)9781479911141

This paper proposes a new detector for interested object detection in high resolution SAR image. To obtain higher performance, four parameters are required to describe features of the interested object. Specifically, the parameters of segmentation scale and average expected object size are used for SAR image segmentation to get patches, which are small image parts to capture fine scale information such as gray variance and shape. In this step, adjacent map is adopted to recode the information and the relationship of all the patches;The third parameter is false alarm rate, which is used to retrieve the intensity threshold for finding out all the potential targets in SAR image;The parameter of object packing density is finally used to reduce the false alarm, which is calculated according to their occurrence frequency. To evaluate the proposed detection methodology, air-born SAR image with one-meter resolution is tested. The scene context of this image is complex, since it includes building, road, vegetation and trucks. If the trucks are taken as the interested objects, this methodology can detect all of them with lower false alarm rate.

关键词： segmentation scale adjacent map object detector high resolution SAR image

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