Image segmentation parameter optimization considering within- and between-segment heterogeneity at multiple scale levels: test case for mapping residential areas using Landsat imagery
Brian Alan Johnson,Milben Bragais,Isao Endo,Damasa B. Magcale-Macandog,Paula Beatrice M. Macandog +4 more
TL;DR: Two approaches for extracting residential areas in Landsat 8 imagery were examined, and naive and parameter-optimized segmentation approaches were compared to assess whether unsupervised segmentation parameter optimization (USPO) could improve the extraction of residential areas.
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Abstract: Multi-scale/multi-level geographic object-based image analysis (MS-GEOBIA) methods are becoming widely-used in remote sensing because single-scale/single-level (SS-GEOBIA) methods are often unable to obtain an accurate segmentation and classification of all land use/land cover (LULC) types in an image. However, there have been few comparisons between SS-GEOBIA and MS-GEOBIA approaches for the purpose of mapping a specific LULC type, so it is not well understood which is more appropriate for this task. In addition, there are few methods for automating the selection of segmentation parameters for MS-GEOBIA, while manual selection (i.e., trial-and-error approach) of parameters can be quite challenging and time-consuming. In this study, we examined SS-GEOBIA and MS-GEOBIA approaches for extracting residential areas in Landsat 8 imagery, and compared naive and parameter-optimized segmentation approaches to assess whether unsupervised segmentation parameter optimization (USPO) could improve the extraction of residential areas. Our main findings were: (i) the MS-GEOBIA approaches achieved higher classification accuracies than the SS-GEOBIA approach, and (ii) USPO resulted in more accurate MS-GEOBIA classification results while reducing the number of segmentation levels and classification variables considerably.
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Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective
TL;DR: An extensive state-of-the-art survey on OBIA techniques is conducted, discussed different segmentation techniques and their applicability to OBIB, and selected optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects.
575
Less is more: optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application
Stefanos Georganos,Taïs Grippa,Sabine Vanhuysse,Moritz Lennert,Michal Shimoni,Stamatis Kalogirou,Eléonore Wolff +6 more
TL;DR: A new metric to perform model selection named classification optimization score (COS) that rewards model simplicity and indirectly penalizes for increased computational time and processing requirements using the number of features for a given classification model as a surrogate is proposed.
208
Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification
TL;DR: An experimental comparison among different architectures of DNNs found that the MLP model was the most accurate classifier, and the integration of CNN and GEOBIA could not lead to more accurate results than the other classifiers applied.
178
An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification
Taïs Grippa,Moritz Lennert,Benjamin Beaumont,Sabine Vanhuysse,Nathalie Stephenne,Eléonore Wolff +5 more
TL;DR: Two GRASS GIS add-ons were developed enabling users to optimize segmentation parameters in an unsupervised manner and to classify remote sensing data using several individual machine learning classifiers or their prediction combinations through voting-schemes.
75
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