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optimal segmentation scale selection and evaluation of cultivated land objects based on high-resolution remote sensing images with spectral and texture features

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第21期28卷 27067-27083页

作者： Lu, Heng Liu, Chao Li, Naiwen Fu, Xiao Li, Longguo Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China Sichuan Univ Coll Hydraul & Hydroelect Engn Chengdu 610065 Peoples R China Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 611756 Peoples R China

As the remote sensing technology develops, there are increasingly more kinds of remote sensing images available from different sensors. High-resolution remote sensing images are widely used in the detection of land cover/land change due to their plenty of characteristics of a specific feature in terms of spectrum, shape, and texture. Current studies regarding cultivated land resources that are the material basis for the human beings to survive and develop focus on the method to accurately obtain the quantity of cultivated land in a region and understand the conditions and the trend of change of the cultivated land. Pixel-based method and object-oriented method are the main methods to extract cultivated land in remote sensing field. Pixel-based method ignores high-level image information, while object-oriented method takes the image spot after image segmentation as the basic unit of information extraction, which can make full use of spectral features, spatial features, semantic features, and contextual features. Image segmentation is a key step of object-oriented method;the core problem is how to obtain the optimal segmentation scale. Traditional methods for determining the optimal segmentation scale of features (such as the homogeneity-heterogeneity method, the maximum area method, and the mean variance method), in which only the spectral and geometrical characteristics are considered, while the textural characteristics are neglected. Based on this, the Quickbird and unmanned aerial vehicle (UAV) images obtained in Xiyu Village, Pengzhou City, Sichuan Province, China, were selected as experimental objects, and the texture mean and spectral grayscale mean method (MANC method based on GLCM), which comprehensively considered the spectrum, shape, and texture features, was proposed to calculate the optimal segmentation scale of cultivated land in the study area. The error segment index (ESI) and centroids distance index (CDI) were adopted to evaluate image segmentation q

关键词： Object-oriented high-resolution image analysis Cultivated land objects optimal segmentation scale Gray-level co-occurrence matrix (GLCM) Spectral-texture feature

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optimal segmentation scale Selection for Object-Based Change Detection in Remote Sensing Images Using Kullback-Leibler Divergence

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2020年第7期17卷 1124-1128页

作者： Wu, Junzheng Li, Biao Ni, Weiping Yan, Weidong Zhang, Han Natl Univ Def Technol Coll Elect Sci Key Lab ATR Changsha 410073 Peoples R China Northwest Inst Nucl Technol Xian 710024 Peoples R China

The scale parameter (SP), to control the sizes of objects, is of great significance in multiscale segmentation which is a prerequisite and foundational step for object-based change detection (OBCD). However, the appropriate SP is not readily apparent and the majority of the existing OBCD algorithms obtain the SPs by empirical or subjective trial-and-error ways that may lead to dissatisfactory accuracy and be time-consuming. To address this issue, an automatic approach for optimal segmentation scale selection for OBCD is proposed in this letter. First, a changed fuzziness image for bitemporal images was generated. Second, multiscale segmentation was implemented in a series of candidate scales, and the merging relationships between adjacent scales were built. Then, mapping the segments to the previous fuzziness image, a statistical metric describing the homogeneity of objects based on the Kullback-Leibler divergence was defined, the increments of the metric between merged objects and their child objects were calculated and weighted to identify the optimal SPs. For performance evaluation, Dempter-Shafer (DS) evidence fusion was utilized in the scales selected by the proposed approach in comparison with other state of art or empirical ones. The experimental results employing GF-1, Google Earth, and aerial images demonstrated the superiority and effectiveness of the SPs identified by the proposed approach in the OBCD task.

关键词： Measurement Merging Image segmentation Remote sensing Task analysis Nickel Object recognition Dempter-Shafer (DS) evidence Kullback-Leibler (KL) divergence object-based change detection (OBCD) optimal segmentation scale remote sensing images

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Calculation of the optimal segmentation scale in object-based multiresolution segmentation based on the scene complexity of high-resolution remote sensing images

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JOURNAL OF APPLIED REMOTE SENSING 2018年第2期12卷

作者： Feng, Tianjing Ma, Hairong Cheng, Xinwen Zhang, Hongping China Univ Geosci Fac Informat Engn Wuhan Hubei Peoples R China Hubei Acad Agr Sci Wuhan Hubei Peoples R China

The quality of multiresolution segmentation directly influences the accuracy of high-resolution remote sensing image classification using object-oriented analysis technology. However, a perfect segmentation scale optimization method has not yet been developed. Using the fact that the optimal segmentation scale of high-resolution remote sensing images is closely related to the complexity of the objects on the image, we propose an approach for calculating the optimal segmentation scale based on the scene complexity of an image. First, we calculate the scene complexity of high-resolution remote sensing images using Watson's vision model. Then, we analyze the relationship between the image scene complexity and the optimal segmentation scale based on the model calculation. optimal segmentation scales are found to be related to the scene complexity of high-resolution remote sensing images by an exponential function, allowing direct calculation of the optimal segmentation scale based on the fitted formulas and the image scene complexity. Finally, we propose a multilevel segmentation strategy to increase the object targeting in the optimal segmentation scale. The optimal segmentation scale calculation method proposed here is simple to perform and has a broad range of potential applications. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： object-based image analysis high-resolution remote sensing images optimal segmentation scale scene complexity

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An Adaptive Image segmentation Method with Automatic Selection of optimal scale for Extracting Cropland Parcels in Smallholder Farming Systems

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REMOTE SENSING 2022年第13期14卷 3067页

作者： Cai, Zhiwen Hu, Qiong Zhang, Xinyu Yang, Jingya Wei, Haodong He, Zhen Song, Qian Wang, Cong Yin, Gaofei Xu, Baodong Huazhong Agr Univ Coll Resources & Environm Macro Agr Res Inst Wuhan 430070 Peoples R China Cent China Normal Univ Coll Urban & Environm Sci Key Lab Geog Proc Anal & Simulat Hubei Prov Wuhan 430079 Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Minist Agr & Rural Affairs Key Lab Agr Remote Sensing AGRIRS Beijing 100081 Peoples R China Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 610031 Peoples R China Chinese Acad Sci State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100101 Peoples R China Beijing Normal Univ Beijing 100101 Peoples R China

Reliable cropland parcel data are vital for agricultural monitoring, yield estimation, and agricultural intensification assessments. However, the inherently high landscape fragmentation and irregularly shaped cropland associated with smallholder farming systems restrict the accuracy of cropland parcels extraction. In this study, we proposed an adaptive image segmentation method with the automated selection of optimal scale (MSAOS) to extract cropland parcels in heterogeneous agricultural landscapes. The MSAOS method includes three major components: (1) coarse segmentation to divide the whole images into homogenous and heterogeneous regions, (2) fine segmentation to determine the optimal segmentation scale based on average local variance function, and (3) region merging to merge and dissolve the over-segmented objects with small area. The potential cropland objects derived from MSAOS were combined with random forest to generate the final cropland parcels. The MSAOS method was evaluated over different agricultural regions in China, and derived results were assessed by benchmark cropland parcels interpreted from high-spatial resolution images. Results showed the texture features of Homogeneity and Entropy are the most important features for MSAOS to extract potential cropland parcels, with the highest separability index of 0.28 and 0.26, respectively. MSAOS-derived cropland parcels had high agreement with the reference dataset over eight tiles in Qichun county, with average F1 scores of 0.839 and 0.779 for the area-based classification evaluation (F-ab) and object-based segmentation evaluation (F-ob), respectively. The further evaluation of MSAOS on different tiles of four provinces exhibited the similar results (F-ab = 0.857 and F-ob = 0.775) with that on eight test tiles, suggesting the good transferability of the MSAOS over different agricultural regions. Furthermore, MSAOS outperformed other widely-used approaches in terms of the accuracy and integrity of the extra

关键词： cropland parcel extraction image segmentation optimal segmentation scale region merging random forest

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Investigation of optimal segmentation Color Space of Bayer True Color Images with Multi-Objective Optimization Methods

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2015年第3期43卷 487-499页

作者： Duan, Guangyao Duan, Fuzhou Xu, Yan Gong, Huili Qu, Xinyuan Capital Normal Univ Dept Coll Resources Environm & Tourism Beijing 100048 Peoples R China

For the selection of the optimal segmentation space of Bayer true color unmanned aerial vehicle image, this paper introduces multi-objectives constraints optimization to solve the inconsistency of multiple indicators. First, the Bayer color images were converted to YIQ(Luninance, Inphase, Quadrature), YCbCr(Luninance, Blue-difference, Red-difference), I1I2I3 (Three linear transformed color-opponent dimensions), HSI(Hue, Saturation, Intensity), Nrgb(Normalized Red, Green, Blue) and CIE(L*a*b*) (Comission Internationale de l'Eclairage, L*a*b* for Lightness and two color-opponent dimensions)color space, then the transformed images were segmented with multi-resolution segmentation method. By introducing the multi-objective constraint function, three parameters such as the topology index, geometric index and spectral area matching index were synthetically considered to determine the optimal segmentation scale. Based on that, the multi-objective constraint function was built to comprehensively analyze the result of segmentation, so as to find out the optimal color space for a certain type of building. And then the global optimum color space appropriate for all kinds of buildings can be gained through the comprehensive analysis of the F value of different types of buildings. Finally a series of images of different acquisition conditions and ground features were selected to conduct the test. The result shows that the optimal segmentation color spaces of different types of buildings vary a little. For cottage the I1I2I3 space can get the excellent object areas that reflect the real edge of the ground features, while the YCbCr space has some advantages on the segmentation of tile-building. Overall, only I1I2I3 color space has better integrated segmentation result for all buildings, and it is considered to be the best color space suitable for segmentation.

关键词： Unmanned Aerial Vehicle(UAV) images optimal segmentation scale optimal color space Multi-objective constraints

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Permanent Snow and Ice Extraction Based on Object-Oriented Classification in Himalaya 23

Permanent Snow and Ice Extraction Based on Object-Oriented C...

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23rd International Conference on Geoinformatics (Geoinformatics)

作者： He, Bing Kong, Bong Chinese Acad Sci Digital Mt & Remote Sensing Applicat Ctr Inst Mt Hazards & Environm Chengdu Peoples R China

ISBN: (纸本)9781467376631

As the highest mountain chain in the world, the Himalaya's snow and ice are melting gradually. How to extract the border and area of permanent snow and ice in Himalaya becomes a very important problem for global climate change research. In order to explore methods to extract permanent snow and ice in unreachable area, this paper takes the multi-source remote sensing data as the basic of multi-scale segmentation, and utilizes the decision tree to probe object-oriented permanent snow and ice extraction. This paper adopts the vector distance method and optimal segmentation scale calculation model to solve the problem of segmentation scale selection. It takes horizontal and vertical distance between the boundary of image objects area after segmentation and the actual boundary of classification targets as the precision difference index, and estimates the effectiveness of segmentation results in order to determine the optimal segmentation scale. Compared with the land-use visual interpretation data of Institute of Mountain Hazards and Environment, the accuracy of extraction results can reach 92.5%. It shows that the proposed methods are feasible, and the results are also credible and accurate.

关键词： optimal segmentation scale Mount Everest Landsat TM Normalized Difference Vegetation Index

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AN IMPROVED OBJECT-ORIENTED CLASSIFICATION METHOD UNDER MULTI-LEVEL FOR FOREST VEGETATION

AN IMPROVED OBJECT-ORIENTED CLASSIFICATION METHOD UNDER MULT...

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第四届全国遥感与地理信息科学研究生论坛

作者： WANG SHUANG College of Information Science Beijing Forestry UniversityBeijing 100083China

The paper used the object-oriented classification to classify the forest which includes single-level classification and multi-level *** taking the Jiangle forest farm as the experiment area and applying the two methods to segment different forest *** the extraction of spectral features and texture features of object segmentation and confirming the threshold by expert knowledge and statistical data,the forest classification was *** comparing the accuracy under two different classification methods,choosing the more appropriate method to get the optimal results.

关键词： Object-oriented classification multi-level classification classification of forest vegetation optimal segmentation scale

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