基于GF-1和Hyperion影像的新增建设用地占用地类信息提取
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摘要
新增建设用地及其占用地类是年度土地利用动态遥感监测的重点关注信息,综合利用多源数据是提高占地信息提取精度的主要手段之一。现有研究基于光学与高光谱特征波段的像素级融合影像开展分析,不可避免地导致了高光谱影像光谱信息的损失。为了尽量避免信息损失,提出一种光学和高光谱影像独立分析、结果集成的新增建设用地占用地类信息提取方法。该方法首先结合土地利用动态遥感监测所定义的3种主要新增建设用地形态,利用前后两个时相GF-1光学影像的光谱和纹理特征提取新增建设用地,其次利用前时相Hyperion高光谱影像的精细光谱成像能力进行详细土地利用分类,最后通过新增建设用地与土地利用分类图的叠置分析提取新增建设用地占用地类。在北京市丰台区典型区域开展的验证实验取得了86.71%的新增建设用地占用地类属性精度,表明本方法可为土地利用动态遥感监测提供较为准确的地类变化信息。
Land occupied by newly increased construction land is an important information concerned by land-use dynamic monitoring using remote sensing. As well known that combined utilization of multi-source data is one of the main methods to improve accuracy of land-use monitoring, however, most of state-of-the-art researches carry out analysis on the pixel-level fused image of visible image and selected bands drawn from hyperspectral image. To avoid the spectral information loss of hyperspectral image this paper proposes a three-fold method which analyzes visible and hyperspectral independently and then combines the analysis results to extract land occupied by newly increased construction land.Firstly, three main types of newly increased construction land in land-use monitoring are introduced, which is then extracted based on the spectral and textural features drawn from bi-temporal visible images. Secondly, detailed land-use mapping is conducted on hyperspectral image of former temporality. Finally, the land-use mapping result is overlaid with the newly increased construction land result to extract occupied land information. Experiments carried out in Fengtai district, Beijing indicate that attribute accuracy of occupied land is up to 86.71%, which proves that the proposed method is able to provide land-use changing information for land-use dynamic monitoring with reliable accuracy.
Land occupied by newly increased construction land is an important information concerned by land-use dynamic monitoring using remote sensing. As well known that combined utilization of multi-source data is one of the main methods to improve accuracy of land-use monitoring, however, most of state-of-the-art researches carry out analysis on the pixel-level fused image of visible image and selected bands drawn from hyperspectral image. To avoid the spectral information loss of hyperspectral image this paper proposes a three-fold method which analyzes visible and hyperspectral independently and then combines the analysis results to extract land occupied by newly increased construction land.Firstly, three main types of newly increased construction land in land-use monitoring are introduced, which is then extracted based on the spectral and textural features drawn from bi-temporal visible images. Secondly, detailed land-use mapping is conducted on hyperspectral image of former temporality. Finally, the land-use mapping result is overlaid with the newly increased construction land result to extract occupied land information. Experiments carried out in Fengtai district, Beijing indicate that attribute accuracy of occupied land is up to 86.71%, which proves that the proposed method is able to provide land-use changing information for land-use dynamic monitoring with reliable accuracy.
引文
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[3]张西雅,金慧然,李培军.多角度高光谱CHRIS/PROBA数据在土地覆盖分类中的应用[J].地理与地理信息科学,2010,26(5):68-71.
[4]曹炳霞.HJ-1A高光谱影像在黄土丘陵区生态环境监测中的应用研究[D].西安:长安大学,2011.
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[6]Liu X Y,Meng Y,Yue A Z,et al.New Normalized Difference Index for Build-up Land Enhancement Using Airborne Visible Infrared Imaging Spectrometer Imagery[J].Journal of Applied Remote Sensing,2014,8(1):85-92.
[7]田养军,薛春纪,马智民,等.曲波变换的高光谱遥感图像融合方法在土地利用调查中的应用[J].遥感学报,2009,13(2):313-319.
[8]尤淑撑,孙毅,李小文.成像光谱技术在土地利用动态遥感监测中的应用研究[J].遥感信息,2005,20(3):31-33.
[9]张定祥,李宪文,杨冀红.基于高光谱和高分辨率影像的土地利用数据库更新研究[J].农业工程学报,2004,20(6):272-276.
[10]柴渊,李万东.土地利用动态遥感监测技术与方法[M].北京:地质出版社,2011.
[11]Huang Q Q,Wang C Y,Meng Y,et al.Urban New Construction Land Parcel Detection with Normalized Difference Vegetation Index and PanTex Information[J].Journal of Applied Remote Sensing,2017,11(2):26-39.
[12]Pesaresi M,Gerhardinger A,Kayitakire Francois.ARobust Built-up Area Index by Anisotropic RotationInvariant Textural Measure[J].IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing,2008,1(3):180-192.
[13]陈静波,刘顺喜,汪承义,等.基于知识决策树的城市水体提取方法研究[J].遥感信息,2013,28(1):29-33.