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

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.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10080533 ◽

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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An Adaptive Contextual SEM Algorithm for Urban Land Cover Mapping Using Multitemporal High-Resolution Polarimetric SAR Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2012.2201448 ◽

2012 ◽

Vol 5 (4) ◽

pp. 1129-1139 ◽

Cited By ~ 30

Author(s):

Xin Niu ◽

Yifang Ban

Keyword(s):

High Resolution ◽

Land Cover ◽

Urban Land ◽

Land Cover Mapping ◽

Polarimetric Sar ◽

Sem Algorithm ◽

Urban Land Cover ◽

Sar Data

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Improving Urban Land Cover/Use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy

Remote Sensing ◽

10.3390/rs12142292 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2292

Author(s):

Xin Luo ◽

Xiaohua Tong ◽

Zhongwen Hu ◽

Guofeng Wu

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Land Cover ◽

Convolutional Neural Network ◽

Training Sample ◽

Urban Land ◽

Support Vector ◽

Land Cover Mapping ◽

Urban Regions ◽

Urban Land Cover

Moderate spatial resolution (MSR) satellite images, which hold a trade-off among radiometric, spectral, spatial and temporal characteristics, are extremely popular data for acquiring land cover information. However, the low accuracy of existing classification methods for MSR images is still a fundamental issue restricting their capability in urban land cover mapping. In this study, we proposed a hybrid convolutional neural network (H-ConvNet) for improving urban land cover mapping with MSR Sentinel-2 images. The H-ConvNet was structured with two streams: one lightweight 1D ConvNet for deep spectral feature extraction and one lightweight 2D ConvNet for deep context feature extraction. To obtain a well-trained 2D ConvNet, a training sample expansion strategy was introduced to assist context feature learning. The H-ConvNet was tested in six highly heterogeneous urban regions around the world, and it was compared with support vector machine (SVM), object-based image analysis (OBIA), Markov random field model (MRF) and a newly proposed patch-based ConvNet system. The results showed that the H-ConvNet performed best. We hope that the proposed H-ConvNet would benefit for the land cover mapping with MSR images in highly heterogeneous urban regions.

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