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遥感技术与应用  2023, Vol. 38 Issue (4): 945-955    DOI: 10.11873/j.issn.1004-0323.2023.4.0945
遥感应用     
基于高分可见光遥感指数的城市阴影高效提取研究
唐晔1,2(),刘小燕1,2,崔耀平1,2(),史志方1,2,邓亮1,2,陈准3
1.河南大学 黄河中下游数字地理技术教育部重点实验室,河南 开封 475004
2.河南大学地理与环境学院,河南 开封 475004
3.河南大学哲学与公共管理学院,河南 开封 475004
Research on Efficient Extraction of Urban Shadow based on High-resolution Visible Light Remote Sensing Index
Ye TANG1,2(),Yaoping CUI1,2,Xiaoyan LIU1,2(),Zhifang SHI1,2,Zhun CHEN1,2,Liang DENG3
1.Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University),Ministry of Education,Kaifeng 475004,China
2.School of Geography and Environmental Science,Henan University,Kaifeng 475004,China
3.School of Philosophy and Public Administration Henan University,Kaifeng 475004,China
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摘要:

高效识别阴影信息是利用阴影和消除阴影的关键前提,有助于城市遥感应用研究的开展,现有关于城市阴影检测多关注在近红外和可见光的多波段合成方面,而对可见光提取阴影的能力检测有待深入。针对这一问题,基于红、绿、蓝(R、G、B)高分卫星影像,结合色彩空间变换和影像多波段运算,研究并提出一种由绿光波段、蓝光波段和亮度分量构建的城市阴影优化指数OUSI (Optimization Urban Shadow Index),从视觉效果及提取精度评估角度进行验证分析。结果表明:OUSI可较完整地提取城市阴影,总体精度达90.46%,高于当前常见的指数法和深度学习阴影检测算法;OUSI受不同土地覆被类型的影响较小,阴影检测结果稳定。与既往基于特征的方法不同,研究构建的阴影指数对原始影像数据仅依赖RGB三波段信息,OUSI指数简洁有效、运算耗时少,进而可以为实现大区域和高精度的城市阴影检测提供切实可行的方案。

关键词: 阴影指数深度学习色彩空间高分影像可见光    
Abstract:

Efficient recognition of shadow information is a key prerequisite for utilizing and eliminating shadows, most of the existing studies on urban shadow detection have been attached to the multi-band synthesis of near-infrared and visible light, while the detection ability of shadows extraction from visible light still remains insufficient. In this study, based on red, green, and blue (R, G, B) high-resolution satellite images, we used color space transformation and image multi-band operation to constructed an Optimization Urban Shadow Index (OUSI) with green light band, blue light band, and luminance component. The visual effect and accuracy evaluation were also be analyzed. The results showed that a more complete urban shadow can be extracted by OUSI with an overall accuracy of 90.46%, outperforming the current common exponential method and deep learning shadow detection algorithms; the shadow detection results were the most stable as it suffered less from the influence of different land cover types. In contrast to the previous feature-based methods, the raw image data of this study only rely on RGB three-band information. The OUSI consumes fewer computing hours and thus providing an effective practical solution to achieve urban shadow detection in large areas.

Key words: Shadow index    Deep learning    Color space    High-resolution image    Visible light
收稿日期: 2022-04-18 出版日期: 2023-09-11
ZTFLH:  TP751.1  
基金资助: 国家自然科学基金项目(42071415);河南省自然科学基金优秀青年科学基金项目(202300410049);信阳生态研究院开放基金(2023XYMS014);河南省研究生教育改革与质量提升工程项目(YJS2023JC22)
通讯作者: 崔耀平     E-mail: tangyhenu@163.com;cuiyp@lreis.ac.cn
作者简介: 唐 晔(1995-),女,河南南阳人,硕士研究生,主要从事遥感大数据与城市规划研究。E?mail: tangyhenu@163.com
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引用本文:

唐晔,刘小燕,崔耀平,史志方,邓亮,陈准. 基于高分可见光遥感指数的城市阴影高效提取研究[J]. 遥感技术与应用, 2023, 38(4): 945-955.

Ye TANG,Yaoping CUI,Xiaoyan LIU,Zhifang SHI,Zhun CHEN,Liang DENG. Research on Efficient Extraction of Urban Shadow based on High-resolution Visible Light Remote Sensing Index. Remote Sensing Technology and Application, 2023, 38(4): 945-955.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2023.4.0945        http://www.rsta.ac.cn/CN/Y2023/V38/I4/945

图1  研究区概况
图2  典型地物光谱特征均值统计
图3  OUSI阴影检测方法流程图
图4  精度分析区:A-F
图5  4种阴影检测方法的结果对比:NSVDI、USI、深度学习、本研究构建的OUSI指数
方法/研究区生产者精度用户精度总体精度
阴影非阴影阴影非阴影
ηs/%ηs/%ρs/%ρs/%τ/%
NSVDI89.6550.0063.4083.3369.49
92.5047.5063.7986.3670.00
66.6750.0057.1060.0058.30
83.3376.6678.1282.1480.00
63.3373.3370.3766.6668.30
90.0080.0081.8188.8885.00
USI86.2093.3392.5987.5089.83
80.0097.5096.9682.9788.75
63.3386.6682.6170.2775.00
86.6696.6696.2987.8791.66
66.6696.6695.2374.3581.66
76.66100.00100.0081.0888.33
深度学习82.7590.0088.8884.3786.40
72.5087.8790.0076.5981.25
46.6696.6693.3364.4471.66
83.3393.3392.5984.8488.33
70.0096.6695.4576.3183.33
66.6690.0086.9572.9778.33
OUSI89.6593.3392.8590.3291.52
82.50100.00100.0085.0091.25
73.3396.6695.6578.3785.00
96.6696.6696.6696.6696.66
76.6696.6695.8380.5586.66
83.33100.00100.0085.7191.66
表1  各精度分析区在NSVDI、USI、深度学习和OUSI的精度评价
研究方法NSVDIUSI深度学习本研究

生产者

精度

阴影ηs/%

非阴影ηn/%

80.91%

62.92%

76.59%

95.14%

70.32%

92.42%

83.69%

97.21%

用户

精度

阴影ρs/%

非阴影ρn/%

69.10%

77.90%

93.95%

80.67%

91.20%

76.59%

96.83%

86.10%

总体精度 方正汇总行τ/%71.85%85.87%81.55%90.46%
表2  研究区在NSVDI、USI、深度学习和OUSI的精度评价
图6  新老城区的阴影面积和占比
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