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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (2) : 56-65     DOI: 10.6046/zrzyyg.2023358
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Hierarchical fine-scale information extraction of bare land based on hue-saturation-value and texture features
WEI Hongyu1,2,3(), YAO Wenju1,2,3(), SUN Jian1,2,3, SUN Song1,2,3, ZHANG Huanxue4
1. Shandong Provincial Lunan Geology and Exploration Institute(Shandong Provincial Bureau of Geology and Mineral Resources No.2 Geological Brigade), Yanzhou 272100, China
2. Shandong Big Data Industry Innovation Center, Yanzhou 272100, China
3. Jining Data and Application Center of High Resolution Earth Observation System, Yanzhou 272100, China
4. College of Geography and Environment, Shandong Normal University, Jinan 250358, China
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Abstract  

Extracting information about bare land is crucial for territorial planning, environmental protection, and sustainable development. However, current information extraction methods for bare land struggle to balance the extraction efficiency and accuracy in large-scale and multitemporal applications. This study constructed normalized difference indices based on the analysis of the hue-saturation-value (HSV) features. By combining texture features and vegetation index, this study proposed a simple, efficient hierarchical fine-scale information extraction method for bare land. This proposed method was applied to the urban area of Qufu City, Shandong Province, China. First, with three GF-1 satellite images as the data source, the red, green, and blue bands from the images were converted to the HSV color space. Based on the differences in H, S, and V components between bare land and other land types, the normalized difference SH and SV indices were constructed for preliminary hierarchical information extraction of bare land. Second, texture features were introduced to low-rise building areas and bare land, where the differences in H, S, and V components are nonsignificant. Different texture features were comparatively analyzed for further information extraction of bare land. Third, the normalized difference vegetation indices were used to achieve the final information extraction of bare land, followed by post-processing of the results. The results of this study demonstrate that the constructed normalized difference indices, combined with homogeneous texture features, showed the optimal extraction performance, with an overall accuracy of above 93% and a Kappa coefficient of above 0.84, outperforming other classification methods. Therefore, the proposed method proves effective in extracting information about bare land, serving as a novel approach for bare land information extraction.
Keywords GF-1      hierarchical fine-scale information extraction of bare land      HSV      texture feature     
ZTFLH:  TP751  
Issue Date: 09 May 2025
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Hongyu WEI
Wenju YAO
Jian SUN
Song SUN
Huanxue ZHANG
Cite this article:   
Hongyu WEI,Wenju YAO,Jian SUN, et al. Hierarchical fine-scale information extraction of bare land based on hue-saturation-value and texture features[J]. Remote Sensing for Natural Resources, 2025, 37(2): 56-65.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023358     OR     https://www.gtzyyg.com/EN/Y2025/V37/I2/56
Fig.1  Study area image and bare land survey examples
Fig.2  Sample data
Fig.3  Flow chart of bare land hierarchical fine extraction technology
影像日期 土堆-
施工工地		 土堆-
未开发用地		 土堆-
休耕的农用地		 施工工地-
未开发用地		 施工工地-
休耕的农用地		 未开发用地-
休耕的农用地
2022-02-26 0.25 0.17 0.22 0.18 0.28 0.21
2022-09-28 0.51 0.21 0.25 0.71 0.41 0.34
2023-06-24 0.63 0.34 0.37 0.54 0.57 0.46
Tab.1  Separability of different types of bare land in the study area
Fig.4  HSV mean curve of typical surface features in the study area
Fig.5  Range of NDSHI and NDSVI index values for typical surface features in the study area
Fig.6  Texture features of low-rise area and bare land
Fig.7  Preliminary results of bare land hierarchical extraction
区域序号 真彩色影像 裸体分层初步
提取结果		 均值 相异性 对比度 方差 同质性
1
2
3
4
Tab.2  Comparison of bare land re-extraction results based on different texture features
Fig.8  Hierarchical fine extraction results of bare land in the urban area of Qufu City
数据序号 土堆 施工工地 未开发用地 休耕的农用地
1
2
3
Tab.3  Extraction results of different types of bare land in the urban area of Qufu City
方法 2022年
2月26日		 2022年
9月28日		 2023年
6月24日
目视
解译
本文
方法
面向
对象
分类
最大
似然
分类
支持
向量
机
Tab.4  Comparison of bare land extraction results
影像日期 指标 本文方法 面向对象分类 最大似然分类 支持向量机
裸地 非裸地 裸地 非裸地 裸地 非裸地 裸地 非裸地
2022年
2月26日		 PA/% 87.65 96.12 81.48 94.29 88.27 88.36 71.60 90.41
UA/% 89.31 95.46 84.08 93.23 73.71 95.32 73.42 89.59
OA/% 93.83 90.83 88.33 85.33
Kappa系数 0.84 0.77 0.72 0.63
2022年
9月28日		 PA/% 83.55 98.88 96.71 93.08 92.62 94.68 91.45 94.64
UA/% 96.21 94.66 82.58 98.82 85.19 97.49 85.28 97.03
OA/% 95.00 94.00 94.17 93.83
Kappa系数 0.86 0.85 0.85 0.84
2023年
6月24日		 PA/% 84.62 98.20 90.38 95.72 89.74 94.37 87.82 94.59
UA/% 94.29 94.78 88.13 96.59 84.85 96.32 85.09 95.67
OA/% 94.67 94.33 93.17 92.83
Kappa系数 0.86 0.85 0.83 0.82
Tab.5  Accuracy evaluation of bare land extraction results in the urban area of Qufu City
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