Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

Jing Ling,Hongsheng Zhang,Yinyi Lin

doi:10.3390/rs13224708

Abstract

Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome the challenge, while optical SAR data fusion is often required due to the limited land surface information provided by SAR. However, the mechanism by which SAR can compensate optical images, given the occurrence of clouds, in order to improve the ULC mapping, remains unexplored. To address the issue, this study proposes a framework, through various sampling strategies and three typical supervised classification methods, to quantify the ULC classification accuracy using optical and SAR data with various cloud levels. The land cover confusions were investigated in detail to understand the role of SAR in distinguishing land cover under different types of cloud coverage. Several interesting experimental results were found. First, 50% cloud coverage over the optical images decreased the overall accuracy by 10–20%, while the incorporation of SAR images was able to improve the overall accuracy by approximately 4%, by increasing the recognition of cloud-covered ULC information, particularly the water bodies. Second, if all the training samples were not contaminated by clouds, the cloud coverage had a higher impact with a reduction of 35% in the overall accuracy, whereas the incorporation of SAR data contributed to an increase of approximately 5%. Third, the thickness of clouds also brought about different impacts on the results, with an approximately 10% higher reduction from thick clouds compared with that from thin clouds, indicating that certain spectral information might still be available in the areas covered by thin clouds. These findings provide useful references for the accurate monitoring of ULC over cloud-prone areas, such as tropical and subtropical cities, where cloud contamination is often unavoidable.

Highlights

Introduction published maps and institutional affilUrban land cover (ULC) is a fundamental Earth observation parameter for understanding the underlying environmental, ecological, and social processes in urban growth [1].ULC dynamics are essential for understanding the structure and function of the Earth’s ecosystem [2,3], providing vital information for numerous urban studies in urban environmental monitoring, urban planning, and urban transport [4,5,6]
To be consistent between the cloud content of the whole image and the proportion of cloudcovered pixels among the labelled pixels in the image, a total of 43,665 pixels of five land cover classes were carefully labelled with visual interpretation over the four images and very high-resolution images from Google Earth near the acquisition date of the satellite images, including 9520 pixels of vegetation (VEG), 6970 pixels of soil (SOI), 6228 pixels of bright impervious surface (BIS), 10,969 pixels of dark impervious surface (DIS), and 9978 pixels of water (WAT)
From the third column, it could be found that in the general distribution of the estimated land covers based on the Synthetic aperture radar (SAR) image close to the classification map of the cloud-free image, there were serious category discontinuities which looked like a number of spots, even though denoising had been performed in the image preprocessing

Summary

Introduction

Introduction published maps and institutional affilUrban land cover (ULC) is a fundamental Earth observation parameter for understanding the underlying environmental, ecological, and social processes in urban growth [1].ULC dynamics are essential for understanding the structure and function of the Earth’s ecosystem [2,3], providing vital information for numerous urban studies in urban environmental monitoring, urban planning, and urban transport [4,5,6]. Urban land cover (ULC) is a fundamental Earth observation parameter for understanding the underlying environmental, ecological, and social processes in urban growth [1]. Owing to the rapid development of Earth observation technologies, various remote sensing data with increasing spatial and spectral resolutions have been widely used for ULC monitoring [7,8,9]. Despite increasing satellite observations, one common problem in the optical remote sensing domain, cloud cover, still hinders the effective application of continuous and accurate urban land change monitoring. As optical remote sensing is a passive technique for Earth observation that relies on solar illumination, the spectral signatures of land covers are affected by the incident component and the reflected component [10].

Methods

Results

Conclusion