Determination of impervious area of Saroor Nagar Watershed of Telangana using spectral indices, MLC, and machine learning (SVM) techniques

Vaddiraju, Shiva Chandra; Reshma, T.; Savitha, Chirasmayee

doi:10.1007/s10661-022-09901-0

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…In recent years, the use of advanced machine learning techniques has significantly improved the detection of impervious surfaces from satellite imagery [26,27]. Techniques such as Classification and Regression Trees (CARTs) [28][29][30], the Random Forest (RF) method [31,32], Artificial Neural Networks (ANNs) [33][34][35][36], and Support Vector Machines (SVMs) [37][38][39][40] are examples of these methodologies.…”

Section: Introductionmentioning

confidence: 99%

Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area

Wu,

Pan

2023

Remote Sensing

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This study utilizes multi-sensor satellite images and machine learning methodology to analyze urban impervious surfaces, with a particular focus on Nanchang, Jiangxi Province, China. The results indicate that combining multiple optical satellite images (Landsat-8, CBERS-04) with a Synthetic Aperture Radar (SAR) image (Sentinel-1) enhances detection accuracy. The overall accuracy (OA) and kappa coefficients increased from 84.3% to 88.3% and from 89.21% to 92.55%, respectively, compared to the exclusive use of the Landsat-8 image. Notably, the Random Forest algorithm, with its unique dual-random sampling technique for fusing multi-sensor satellite data, outperforms other machine learning methods like Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Classification and Regression Trees (CARTs), Maximum Likelihood Classification (Max-Likelihood), and Minimum Distance Classification (Min-Distance) in impervious surface extraction efficiency. With additional satellite images from 2015, 2017, and 2020, the impervious surface changes are tracked in the Nanchang metropolitan region. From 2015 to 2021, they record a notable increase in impervious surfaces, signaling a quickened urban expansion. This study observes several impervious surface growth patterns, such as a tendency to concentrate near rivers, and larger areas in the east of Nanchang. While the expansion was mainly southward from 2015 to 2021, by 2021, the growth began spreading northward around the Gan River basin.

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