Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling

Abstract

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.

From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

“…It may be a very time-consuming and labor-intensive process to directly extract the land use map for the entire study area using the object-based approach. Firstly, due to the variance in the shape, size, color, and other properties of objects of different classes or even within each class, using a universal scale parameter for segmentation is not often helpful to extract all types of land cover [56] and/or land use. It is necessary to use a multi-scale approach to achieve the best result; however, this approach may require a great deal of time and effort to determine appropriate parameters.…”

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

“…To overcome this problem, automatic approaches are widely used to generate optimal segmentation results. In several studies, it has been emphasized that different LULCs have their own inherent scales [6,28,49]. It is therefore often better to segment an image using multiple SPs rather than a universal one to extract different LULCs of interest [46].…”

Qualitative Rating of Lossy Compression for Aerial Imagery by Neutrosophic WASPAS Method

“…The task is to identify the important visual features that were lost during lossy compression. The features can be characterized by generalizations like shape, size, density, color tone, texture [22,23]. Various land covers, like vegetation, sand, or water, have distinct textures and colors in the aerial images.…”