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

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.

Detecting the Planform Changes Due to the Seasonal Flow Fluctuation and 2012 Severe Flood in the Amazon River near Iquitos City, Peru Based on Remote Sensing Image Analysis

“…Individual images within the period of January to March (the wet season) and in September (the dry season) were used to visualize the overall scenery of the study area. Clouds have been found in previous studies to reduce land cover classification accuracy because they can significantly affect the accuracy of land cover classification [35,36]. As suggested by Chavez [37] and Milanović et al [38], the cloud cover of images used for analysis was less than 20%.…”

Overview of the Special Issue on Applications of Remote Sensing Imagery for Urban Areas

“…This Special Issue (SI) aims to invite recent advances in the applications of RS imagery for urban areas, and 17 papers in total were selected and published. Among them, 12 papers emphasize the novel urban application algorithms based on RS imageries, such as urban attribute mapping, building extraction, classification, change detection, and so on [1][2][3][4][5][6][7][8][9][10][11][12], and 5 papers directly employed RS imageries to analyze the environmental variations and urban expansion in typical cities, such as urban heat island, air pollution, lightning, and so on [13][14][15][16][17].…”

“…Except for building information extraction, classification, target detection, and change detection are also very important for urban applications using RS imageries, and there are five papers on these issues [5][6][7][8][9]. As for classification, Ling et al [5] proposed a research framework to quantify the urban land cover (ULC) classification accuracy using optical and SAR data with various cloud levels, using three typical supervised classification methods.…”

“…Except for building information extraction, classification, target detection, and change detection are also very important for urban applications using RS imageries, and there are five papers on these issues [5][6][7][8][9]. As for classification, Ling et al [5] proposed a research framework to quantify the urban land cover (ULC) classification accuracy using optical and SAR data with various cloud levels, using three typical supervised classification methods. The experimental results indicate that the ULC classification accuracy decreases with increasing cloud content, and the fusion of SAR and optical data can significantly help reduce the confusion between land covers under clouds and improve the classification accuracy.…”

“…New methodologies are presented for extracting the building information, modeling urban attributes and population, and detecting the urban flooding using remote sensing imageries [1][2][3][4]10,12]. Novel models for RS classification, target and change detection are also included, which can significantly support further urban applications [5][6][7][8][9]. Different analyses on urban expansion, urban heat islands, air pollution, and lightning can advance our understanding of the interactions between urban development and the regional environment [13][14][15][16][17].…”