Subpixel impervious surface estimation in the Nansi Lake Basin using random forest regression combined with GF-5 hyperspectral data,Journal of Applied Remote Sensing

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Subpixel impervious surface estimation in the Nansi Lake Basin using random forest regression combined with GF-5 hyperspectral data
Journal of Applied Remote Sensing ( IF 1.4 ) Pub Date : 2020-09-01 , DOI: 10.1117/1.jrs.14.034515
Jiantao Liu ₁ , Chunting Liu ₁ , Quanlong Feng ₂ , Yin Ma ₁

Affiliation

Abstract. Impervious surfaces (ISs) are significant indicators of the overall environmental health of watersheds. Monitoring the IS spatial patterns of watersheds is helpful for improving water management and regional pollution assessment. A random forest regression (RFR) approach was developed to estimate the subpixel IS percentage (ISP) from Chinese GF-5 hyperspectral imagery in 30 m pixels in the Nansi Lake Basin. Initially, object-based image analysis and overlay analysis were adopted to generate a very high-resolution ISP reference dataset using GF-1 multispectral imagery. Subsequently, an RFR-based ISP prediction model was established and evaluated using training and validation samples, respectively. The experimental results demonstrated that the proposed method shows a good performance with a root mean square error of 0.17, a mean absolute error of 0.15, and an R2 value of 0.89. Meanwhile, both band 10 and band 53 of GF-5 made the highest contributions to ISP modeling. Further comparison with other machine learning algorithms revealed that the RFR-based ISP model outperforms support vector machine regression and partial least squares regression, demonstrating the potential usability of the proposed method in ISP estimation from hyperspectral imagery.

更新日期：2020-09-01

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