Abstract

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.

Highlights

  • IntroductionFine scale urban land cover information is valuable for a wide range of applications, including the analysis of urban green space accessibility [1], urban hydrology [2], and urban heat island effect [3]

  • After discarding the papers that did not use the multiresolution segmentation (MRS) for segmentation, the main challenge was with the papers that did not report some values related to MRS parameters, image spatial resolutions, and/or image radiometric resolutions

  • We conducted a meta-analysis to investigate the potential of employing information derived from past Geographic Object-Based Image Analysis (GEOBIA) urban land cover mapping studies to select appropriate, i.e., reasonably accurate, segmentation parameters ( the scale parameter (SP)) for the commonly used multiresolution segmentation (MRS) algorithm

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Summary

Introduction

Fine scale urban land cover information is valuable for a wide range of applications, including the analysis of urban green space accessibility [1], urban hydrology [2], and urban heat island effect [3]. The advent of remote and sensing very high resolution (VHR). Imagery has significantly the effect [3]. The adventsensing of remote and very high resolution imagery hasfacilitated significantly procedure and updating urban land cover maps. The classification of urban facilitatedfor theproducing procedure for producing and updating urban land cover maps. The land cover objects of interest (e.g., roads, soil) intrees, VHRgrass, images based classification of urban land cover objectsbuildings, of interesttrees,

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