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

2021 ◽  
Vol 13 (22) ◽  
pp. 4708
Author(s):  
Jing Ling ◽  
Hongsheng Zhang ◽  
Yinyi Lin
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Urban Land ◽  
Sar Images ◽  
Cloud Coverage ◽  
Urban Land Cover ◽  
Optical Images ◽  
Training Samples ◽  
Sar Data ◽  
Supervised Classification Methods

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.

Classification of Polarimetric SAR Data Based on Object-Based Multiple Classifiers for Urban Land-Cover

2016 ◽  
Vol 44 (6) ◽  
pp. 855-863 ◽  
Author(s):  
Masoud Habibi ◽  
Mahmod Reza Sahebi ◽  
Yasser Maghsoudi ◽  
Shaheen Ghayourmanesh
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Polarimetric Sar ◽  
Multiple Classifiers ◽  
Urban Land Cover ◽  
Object Based ◽  
Sar Data

Quantification of Urban Heat Intensity with Land Use/Land Cover Changes Using Landsat Satellite Data Over Urban Landscapes

2021 ◽  
Author(s):  
Ruchi Bala ◽  
Rajendra Prasad ◽  
Vijay Pratap Yadav
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Urban Land ◽  
Land Cover Changes ◽  
Urban Landscapes ◽  
Positive Contribution ◽  
Negative Contribution ◽  
Urban Land Cover ◽  
Urban Heat ◽  
Landsat Satellite

Abstract Urban heat island (UHI) is a phenomenon which may have adverse effects on our environment and is stimulated as a result of urbanisation or land cover changes. Thermal remote sensing has been found beneficial to study the effect of urbanisation on UHI intensity. This paper analyses the variation in Land surface temperature (LST) with land cover changes in Varanasi city of India from 1989 to 2018 using Landsat satellite images. A new index named Urban Heat Intensity Ratio Index (UHIRI) was proposed to quantify the urban heat intensity from 1989 to 2018 which was found to increase from 0.36 in year 1989 to 0.87 in year 2018. Further, contribution of each land cover towards UHI was determined using Land cover contribution index (LCCI). The negative value of LCCI for water and vegetation indicates its negative contribution towards UHI whereas positive value of LCCI for bare soil and built-ups depicted its positive contribution towards UHI. The LCCI value for urban land cover shows significant increase in 29 years i.e. 0.49, 1.43, 3.40, 4.37 for years 1989, 1997, 2008 and 2018 respectively. The change in normalized LST from years 1989 to 2018 for the conversion of bare land to built-ups and vegetation to built-ups were found to be as -0.11 and 0.42 respectively. This led to conclusion that the replacement of vegetation with urban land cover has severe impact on increasing UHI intensity.

Improving Urban Land Cover Mapping with the Fusion of Optical and SAR Data Based on Feature Selection Strategy

2022 ◽  
Vol 88 (1) ◽  
pp. 17-28
Author(s):  
Qing Ding ◽  
Zhenfeng Shao ◽  
Xiao Huang ◽  
Orhan Altan ◽  
Yewen Fan
Keyword(s):  
Feature Selection ◽  
Land Cover ◽  
Urban Land ◽  
Model Complexity ◽  
Selection Strategy ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Selection Methods ◽  
Urban Land Cover ◽  
Sar Data

Taking the Futian District as the research area, this study proposed an effective urban land cover mapping framework fusing optical and SAR data. To simplify the model complexity and improve the mapping results, various feature selection methods were compared and evaluated. The results showed that feature selection can eliminate irrelevant features, increase the mean correlation between features slightly, and improve the classification accuracy and computational efficiency significantly. The recursive feature elimination-support vector machine (RFE-SVM) model obtained the best results, with an overall accuracy of 89.17% and a kappa coefficient of 0.8695, respectively. In addition, this study proved that the fusion of optical and SAR data can effectively improve mapping and reduce the confusion between different land covers. The novelty of this study is with the insight into the merits of multi-source data fusion and feature selection in the land cover mapping process over complex urban environments, and to evaluate the performance differences between different feature selection methods.

scholarly journals Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery

2021 ◽  
Vol 10 (8) ◽  
pp. 533
Author(s):  
Bin Hu ◽  
Yongyang Xu ◽  
Xiao Huang ◽  
Qimin Cheng ◽  
Qing Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Urban Land ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Sensing Data ◽  
Urban Land Cover ◽  
Sar Data

Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery.

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