Assessing Machine Learning Based Supervised Classifiers For Built-Up Impervious Surface Area Extraction From Sentinel-2 Images

Misra, Mandvi; Kumar, Deepak; Shekhar, Sulochana

doi:10.1016/j.ufug.2020.126714

Cited by 29 publications

(17 citation statements)

References 31 publications

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“…With the advancement of remote sensing technology, satellite images have become one of the key data sources for mapping and monitoring ISA dynamics [7]. Commonly used remotely sensed data include MODIS [8], [9], Landsat [10], Sentinel-2 [11], [12], IKONOS [13], [14], to list a few. As medium-resolution images can provide reliable ISA estimations [1], 30m Landsat products have been widely used to extract and characterize the dynamics of impervious surfaces [2], [7], [9].…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results revealed that more details and higher accuracy ISA are obtained from Sentinel-2 images [18]. Misra et al [11] evaluated the performances of three supervised classification methods for the generation of land use and land cover maps from Sentinel-2 images. In addition, they made a zonal analysis to understand the impacts of extracted built-up impervious surfaces on rising environmental issues.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Analysis of Index-Based Methods for Impervious Surface Mapping Using Multiseasonal Sentinel-2 Satellite Data

Shao

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Studies have shown that Sentinel-2 images have advantages over Landsat images in impervious surface area (ISA) extraction. The performance of index-based methods can be affected by different binary methods and subject to seasonal variation. This study marks the first attempt to assess the performance of different spectral indices for ISA extraction using multi-seasonal Sentinel-2 images. Specifically, five indices (i.e., the Biophysical Composition Index calculated using the Gram-Schmidt orthogonalization method (BCI_GSO), Biophysical Composition Index calculated using a principal component-based Procrustes analysis (BCI_PCP), Normalized Built-up Area Index (NBAI), Combinational Build-up Index (CBI), and Perpendicular Impervious Surface Index (PISI)) and three impervious surface binary methods (i.e., Otsu's method, manual method, and ISODATA (Iterative Self-Organizing Data Analysis Technique Algorithm) classification method) were tested on multi-seasonal Sentinel-2 images in the main urban area of Wuhan, China. Results showed that PISI combined with the ISODATA classification method achieved the highest accuracy with 92.64% OA, and 0.8410 Kappa coefficient, and NBAI combined with Otsu's method achieved the lowest accuracy with 35.37% OA, and 0.013 Kappa coefficient. Regarding the seasonal sensitivity, PISI is relatively more stable than the other methods. The superior performance of PISI is largely due to its capability in separating ISA from soil and vegetation. In addition, summer is the best season to map ISA from Sentinel-2 images when the impervious surface is generally less confused with bare soil. This study serves as a reference for the selection of spectral indices for

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Index-Based Methods for Impervious Surface Mapping Using Multiseasonal Sentinel-2 Satellite Data

Shao

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…Due to such reasons, up-to-date information regarding impervious surface is of paramount importance for supporting urban land management/planning, detection of unplanned built-up areas, study of regional land-use pattern, and ecosystem monitoring [5,[7][8][9][10]. In developing countries including Vietnam, the conventional approach for obtaining such information is field survey.…”

Section: Introductionmentioning

confidence: 99%

“…ese technologies have been proven to be viable tools for surveying urban landscapes which are rapidly changing and providing timely information regarding urban growth [26][27][28][29]. Based on remotely sensed images, statistical and machine learning models can be constructed for automatic impervious surface extraction [8].…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al [36] investigated the use of the 10 m resolution Sentinel-2A dataset for impervious area extraction and pointed out the superiority of high-resolution data over the conventional 30 m resolution Landsat dataset. Misra et al [8] attempted to employ high-resolution image obtained from Sentinel-2 to improve the quality of impervious surface detection result; the authors rely on spectral angle mapper, support vector machine, and neural network to carry out pattern recognition task.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

Hoang

2021

Computational Intelligence and Neuroscience

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Up-to-date information regarding impervious surface is valuable for urban planning and management. The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov-accelerated adaptive moment estimation (Nadam), Adam with decoupled weight decay (AdamW), and a new exponential moving average variant (AMSGrad) are used to train the artificial neural network models employed for impervious surface detection. These advanced optimizers are benchmarked with the conventional gradient descent with momentum (GDM). Remotely sensed images collected from Sentinel-2 satellite for the study area of Da Nang city (Vietnam) are used to construct and verify the proposed approach. Moreover, texture descriptors including statistical measurements of color channels and binary gradient contour are employed to extract useful features for the neural computing model-based pattern recognition. Experimental result supported by statistical test points out that the Nadam optimizer-based neural computing model has achieved the most desired predictive accuracy for the data collected in the studied region with classification accuracy rate of 97.331%, precision = 0.961, recall = 0.984, negative predictive value = 0.985, and F1 score = 0.972. Therefore, the model developed in this study can be a helpful tool for decision-makers in the task of urban land-use planning and management.

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Machine Learning Techniques for the Semiautomated Recognition of Urban and Peri-Urban Configurations

Dato

Falasca

Marucci

2023

Computational Science and Its Applications – ICCSA 2023 Workshops

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Assessing Machine Learning Based Supervised Classifiers For Built-Up Impervious Surface Area Extraction From Sentinel-2 Images

Cited by 29 publications

References 31 publications

A Comparative Analysis of Index-Based Methods for Impervious Surface Mapping Using Multiseasonal Sentinel-2 Satellite Data

A Comparative Analysis of Index-Based Methods for Impervious Surface Mapping Using Multiseasonal Sentinel-2 Satellite Data

Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

Machine Learning Techniques for the Semiautomated Recognition of Urban and Peri-Urban Configurations

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