scholarly journals Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region

2017 ◽  
Vol 7 (1) ◽  
pp. 3 ◽  
Author(s):  
Tao Zhou ◽  
Meifang Zhao ◽  
Chuanliang Sun ◽  
Jianjun Pan
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Monsoon Climate ◽  
Climate Region ◽  
Urban Land Cover ◽  
Subtropical Monsoon Climate ◽  
The Impact

scholarly journals IMPLEMENTATION OF THE MARKOV RANDOM FIELD FOR URBAN LAND COVER CLASSIFICATION OF UAV VHIR DATA

2016 ◽  
Vol 3 (2) ◽  
pp. 127
Author(s):  
Jati Pratomo ◽  
Triyoga Widiastomo
Keyword(s):  
Land Cover ◽  
Random Field ◽  
Markov Random Field ◽  
Optimal Parameter ◽  
Land Cover Classification ◽  
Urban Land ◽  
Urban Land Cover ◽  
Markov Random ◽  
Atmospheric Variations ◽  
The Impact

The usage of Unmanned Aerial Vehicle (UAV) has grown rapidly in various fields, such as urban planning, search and rescue, and surveillance. Capturing images from UAV has many advantages compared with satellite imagery. For instance, higher spatial resolution and less impact from atmospheric variations can be obtained. However, there are difficulties in classifying urban features, due to the complexity of the urban land covers. The usage of Maximum Likelihood Classification (MLC) has limitations since it is based on the assumption of the normal distribution of pixel values, where, in fact, urban features are not normally distributed. There are advantages in using the Markov Random Field (MRF) for urban land cover classification as it assumes that neighboring pixels have a higher probability to be classified in the same class rather than a different class. This research aimed to determine the impact of the smoothness (λ) and the updating temperature (Tupd) on the accuracy result (κ) in MRF. We used a UAV VHIR sized 587 square meters, with six-centimetre resolution, taken in Bogor Regency, Indonesia. The result showed that the kappa value (κ) increases proportionally with the smoothness (λ) until it reaches the maximum (κ), then the value drops. The usage of higher (Tupd) has resulted in better (κ) although it also led to a higher Standard Deviations (SD). Using the most optimal parameter, MRF resulted in slightly higher (κ) compared with MLC.

Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping

2020 ◽  
Vol 247 ◽  
pp. 111945 ◽  
Author(s):  
Yindan Zhang ◽  
Gang Chen ◽  
Jelena Vukomanovic ◽  
Kunwar K. Singh ◽  
Yong Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Shadow Removal ◽  
Attention Model ◽  
Urban Land Cover

A Novel Unsupervised Sample Collection Method for Urban Land-Cover Mapping Using Landsat Imagery

2019 ◽  
Vol 57 (6) ◽  
pp. 3933-3951 ◽  
Author(s):  
Jiayi Li ◽  
Xin Huang ◽  
Ting Hu ◽  
Xiuping Jia ◽  
Jon Atli Benediktsson
Keyword(s):  
Land Cover ◽  
Landsat Imagery ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Sample Collection ◽  
Collection Method ◽  
Urban Land Cover

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.

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