Hierarchical Object-Based Mapping of Urban Land Cover Using Sentinel-2 Data: A Case Study of Six Cities in Central Europe

Bobáľová, Hana; Benová, Alexandra; Kožuch, Miroslav

doi:10.1007/s41064-020-00135-8

Cited by 4 publications

(2 citation statements)

References 53 publications

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“…Therefore, instead of finding a suitable starting point in numerous potential segmentation results, it is more reasonable and time-saving to use the segmentation results in the section sequence as the starting point for optimization. Output BTL 1 MC are used to calculate the dissimilarity of adjacent regions. 2 pq is an ordered linear table.…”

Section: Scale-sets Modelmentioning

confidence: 99%

“…Object-based image analysis (OBIA) has been widely used in the past two decades, such as in urban-land-cover mapping [1][2][3], ecological monitoring [4][5][6], disaster evaluation [7][8][9], and crop-type identification [10][11][12]. In recent years, OBIA has also been incorporated into the deep learning model to capture the precise outlines of ground objects and model spatial-context relationships [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

et al. 2021

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Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.

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Section: Scale-sets Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

et al. 2021

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Understanding the role of training sample size in the uncertainty of high-resolution LULC mapping using random forest

Phinzi,

Ngetar,

Pham

et al. 2023

Earth Sci Inform

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High-resolution sensors onboard satellites are generally reputed for rapidly producing land-use/land-cover (LULC) maps with improved spatial detail. However, such maps are subject to uncertainties due to several factors, including the training sample size. We investigated the effects of different training sample sizes (from 1000 to 12,000 pixels) on LULC classification accuracy using the random forest (RF) classifier. Then, we analyzed classification uncertainties by determining the median and the interquartile range (IQR) of the overall accuracy (OA) values through repeated k-fold cross-validation. Results showed that increasing training pixels significantly improved OA while minimizing model uncertainty. Specifically, larger training samples, ranging from 9000 to 12,000 pixels, exhibited narrower IQRs than smaller samples (1000–2000 pixels). Furthermore, there was a significant variation (Chi2 = 85.073; df = 11; p < 0.001) and a significant trend (J-T = 4641, p < 0.001) in OA values across various training sample sizes. Although larger training samples generally yielded high accuracies, this trend was not always consistent, as the lowest accuracy did not necessarily correspond to the smallest training sample. Nevertheless, models using 9000–11,000 pixels were effective (OA > 96%) and provided an accurate visual representation of LULC. Our findings emphasize the importance of selecting an appropriate training sample size to reduce uncertainties in high-resolution LULC classification.

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A Geospatial Approach to Mapping and Monitoring Real Estate-Induced Urban Expansion in the National Capital Region of Delhi

Naikoo,

Shahfahad,

Talukdar

et al. 2024

PFG

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Hierarchical Object-Based Mapping of Urban Land Cover Using Sentinel-2 Data: A Case Study of Six Cities in Central Europe

Cited by 4 publications

References 53 publications

Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

Understanding the role of training sample size in the uncertainty of high-resolution LULC mapping using random forest

A Geospatial Approach to Mapping and Monitoring Real Estate-Induced Urban Expansion in the National Capital Region of Delhi

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