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来源类型Peer-reviewed Article
规范类型其他
Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling
Brian JOHNSON
发表日期2018-01
出版者MDPI AG
出版年2018
页码73
语种英语
概述The advent of very high resolution (VHR) satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA) have led to many new opportunities for fine-scale...
摘要

The advent of very high resolution (VHR) satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA) have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. Image segmentation is an important step in the GEOBIA framework, so great time/effort is often spent to ensure that computer-generated image segments closely match real-world objects of interest. In the remote sensing community, segmentation is frequently performed using the multiresolution segmentation (MRS) algorithm, which is tuned through three user-defined parameters (the scale, shape/color, and compactness/smoothness parameters). The scale parameter (SP) is the most important parameter and governs the average size of generated image segments. Existing automatic methods to determine suitable SPs for segmentation are scene-specific and often computationally intensive, so an approach to estimating appropriate SPs that is generalizable (i.e., not scene-specific) could speed up the GEOBIA workflow considerably. In this study, we attempted to identify generalizable SPs for five common urban land cover types (buildings, vegetation, roads, bare soil, and water) through meta-analysis and nonlinear regression tree (RT) modeling. First, we performed a literature search of recent studies that employed GEOBIA for urban land cover mapping and extracted the MRS parameters used, the image properties (i.e., spatial and radiometric resolutions), and the land cover classes mapped. Using this data extracted from the literature, we constructed RT models for each land cover class to predict suitable SP values based on the: image spatial resolution, image radiometric resolution, shape/color parameter, and compactness/smoothness parameter. Based on a visual and quantitative analysis of results, we found that for all land cover classes except water, relatively accurate SPs could be identified using our RT modeling results. The main advantage of our approach over existing SP selection approaches is that our RT model results are not scene-specific, so they can be used to quickly identify suitable SPs in other VHR images.

主题Ecosystems & Natural Resources$Sustainable Cities & Societies
区域Worldwide
URLhttps://pub.iges.or.jp/pub/identifying-generalizable-image-segmentation
来源智库Institute for Global Environmental Strategies (Japan)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/311579
推荐引用方式
GB/T 7714
Brian JOHNSON. Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling. 2018.
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