| 基于监督与非监督分割评价方法提取高分辨率遥感影像特定目标地物的对比研究 |
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| 引用本文： | 张寅丹,王苗苗,陆海霞,刘勇.基于监督与非监督分割评价方法提取高分辨率遥感影像特定目标地物的对比研究[J].地球信息科学,2019,21(9):1430-1443. |
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| 作者姓名： | 张寅丹 王苗苗 陆海霞 刘勇 |
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| 作者单位： | 兰州大学资源环境学院,兰州730000;北卡罗莱纳州立大学地理空间分析中心,罗利27606;兰州大学资源环境学院,兰州,730000 |
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| 基金项目： | 国家自然科学基金项目(41271360);兰州大学中央高校基本科研业务费专项资金项目(LZUJBKY-2016-248) |
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| 摘 要： | 基于地理对象的影像分析方法已成为高分辨率遥感影像分析的重要手段。影像分割作为其关键步骤,如何设置合适的分割算法参数对后续分割和分类结果有重要的影响。目前分割参数优选方法的探讨分别从非监督与监督分割质量评价2个方面展开,而何者更适合高分辨率遥感影像特定目标地物分析仍缺乏对比研究。本文以城镇和乡村为例,选取多源遥感数据Quickbird、WorldView-2和ALOS影像中共有的3种典型土地覆被为研究对象,基于2种具有代表性的非监督与监督方法ESP2 (Estimation of Scale Parameter 2)与ED2 (Euclidean distance 2) 进行实验,对最优分割和分类的结果进行全面的对比分析。结果表明：① 相同实验参数下,监督方法均能以较少的分割数据集获得目标地物的最优分割结果,且与真实地理对象吻合度更高;② 非监督方法依靠影像自身特征分析进行分割参数优选,无法克服不同景观格局和影像分辨率的影响,而监督方法可通过改变参考数据集的面积和空间分布模式等来降低其影响;③ 非监督方法往往因为欠分割而漏分小目标地物,这样会严重影响局部分类结果。虽然本文中非监督与监督方法的整体分类精度均可达90.08%以上,但非监督方法的漏分率却是监督方法的1.43~4.65倍。因此,本研究认为监督方法更适合分析高分辨率遥感影像特定小目标地物。
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| 关 键 词： | 基于对象的影像分析 分割参数优选 特定地物分析 非监督 监督 对比研究 |
| 收稿时间： | 2018-12-04 |
Comparing Supervised and Unsupervised Segmentation Evaluation Methods for Extracting Specific Land Cover from High-Resolution Remote Sensing Imagery |
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| ZHANG Yindan,WANG Miaomiao,LU Haixia,LIU Yong.Comparing Supervised and Unsupervised Segmentation Evaluation Methods for Extracting Specific Land Cover from High-Resolution Remote Sensing Imagery[J].Geo-information Science,2019,21(9):1430-1443. |
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| Authors: | ZHANG Yindan WANG Miaomiao LU Haixia LIU Yong |
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| Institution: | 1. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China;2. Center for Geospatial Analytics, North Carolina State University, Raleigh NC 27606, USA |
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| Abstract: | Geographic Object-Based Image Analysis (GEOBIA), as a new paradigm, can achieve higher accuracy than pixel-based image analysis for high-resolution remote sensing imagery. Image segmentation plays an important role throughout the course of the GEOBIA. Multi-resolution segmentation (MRS) algorithm is widely used to segment an image into meaningful objects. It is a bottom-up region merging process integrated into eCognition software, which includes three key parameters (i.e. scale, shape, and compactness) to determine the size and boundary of the image objects. As a key step of GEOBIA, how to select the appropriate segmentation parameter values in MRS remains a challenge, and has an important influence on the subsequent segmentation and classification results. Previous studies focused on segmentation parameters optimization using either unsupervised or supervised methods. However, which method (i.e. unsupervised and supervised) is more suitable for analyzing specific land covers of high-resolution remote sensing imagery is still underexplored. To close the gap, we compared the optimal segmentation and classification results based on unsupervised and supervised methods. Meanwhile, we tested with three land cover types (i.e., farmland, residential area, and pond), and choose two representative segmentation parameter optimization methods for unsupervised and supervised methods, including Estimation of Scale Parameter 2 (ESP2) and Euclidean Distance 2 (ED2). The multi-source high-resolution remote sensing data (i.e., Quickbird, Worldview-2, and ALOS) were used to validate the robustness and generalizability of the unsupervised and supervised methods. We found that for a certain land cover category, the boundary of segments obtained by the supervised method seemed more consistent with the geo-objects in real world, while the optimized parameters were too large to extract the small area of geo-objects for the unsupervised method, leading to the lower classification accuracy. The supervised method performed better in analyzing the segmentation parameters optimization of the geo-object using the referenced data of land cover categories, at the same time which can break out the effect of different landscape and image resolution via reference dataset optimization, while the unsupervised method depended on image features, the artificial visual interpretation and had lower recognition accuracy due to higher subjectivity and uncertainty in land cover category identification. Although the overall classification accuracy is still above 90.08%, the omission rate is 1.43~4.65 times to the supervised method. Comparing the two methods, the supervised method obtained the optimal segmentation results with higher efficiency and accuracy using fewer segment datasets in both segmentation and classification results than the unsupervised method. Our findings suggest that the supervised method is more suitable for mapping specific land covers with high-resolution remote sensing imagery. |
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| Keywords: | Geographic Object-Based Image Analysis(GEOBIA) segmentation parameter optimization unsupervised supervised specific land cover analysis a comparative study |
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