基于GF-2遥感影像的一种快速水体信息提取方法
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摘要
在高分辨率遥感影像中,水体与阴影(尤其是高大建筑物阴影)、暗色地物不易区分。针对GF-2遥感影像的光谱特性的大量实验研究,提出了一种新综合水体指数法(NCWI)来增强水体区域信息;同时利用改进的OSTU结合鸡群算法(CSO)快速自适应地确定最佳分割阈值,进而得到最终的水体区域。将其同归一化NDWI、改进谱间关系法、主成分分析综合法等常见水体信息提取方法应用于GF-2遥感影像水体信息提取,利用采用实地采样和人工解译的混淆矩阵对提取的水体区域结果进行精度验证和对比分析,从而验证了其有效性和高效性。4个实验区域的结果证明,该算法可以快速有效地提取水体信息,精确度分别达到97.82%,97.44%,92.13%,96.94%。
It is difficult to distinguish water from shadow(especially the shadows of tall buildings)and dark ground objects in high resolution remote sensing images, especially in GF-2 remote sensing images. This study analyzes the spectral features of typical terrains of the GF-2 remote sensing images through a lot of experiments. A new comprehensive water index method(NCWI) is proposed to enhance water body region information; and then, the improved method of maximum between-class variance(OSTU) combining with the chicken swarm optimization algorithm(CSO) are used to quickly and adaptively determine the optimal segmentation threshold to obtain the final water body region. To demonstrate the effectiveness of the proposed algorithm, the method of NDWI algorithm, the multi-band spectrum-photometric algorithm and the principal component analysis synthesis algorithms are used for comparison in water-body extraction. The confusion matrix and the field sampling are applied as the statistical metric to quantitatively evaluate the performance of the algorithms mentioned above. The verification results indicate that the new method can be used to extract quickly and effectively extract water body information, and the accuracy reached 97.82%,97.44%, 92.13%, 96.94% respectively.
It is difficult to distinguish water from shadow(especially the shadows of tall buildings)and dark ground objects in high resolution remote sensing images, especially in GF-2 remote sensing images. This study analyzes the spectral features of typical terrains of the GF-2 remote sensing images through a lot of experiments. A new comprehensive water index method(NCWI) is proposed to enhance water body region information; and then, the improved method of maximum between-class variance(OSTU) combining with the chicken swarm optimization algorithm(CSO) are used to quickly and adaptively determine the optimal segmentation threshold to obtain the final water body region. To demonstrate the effectiveness of the proposed algorithm, the method of NDWI algorithm, the multi-band spectrum-photometric algorithm and the principal component analysis synthesis algorithms are used for comparison in water-body extraction. The confusion matrix and the field sampling are applied as the statistical metric to quantitatively evaluate the performance of the algorithms mentioned above. The verification results indicate that the new method can be used to extract quickly and effectively extract water body information, and the accuracy reached 97.82%,97.44%, 92.13%, 96.94% respectively.
引文
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[13]袁欣智,江洪,陈芸芝,等.一种应用大津法的自适应阈值水体提取方法[J].遥感信息,2016,31(5):36-42.
[2]童李霞,燕琴,骆成凤,等.基于NDWI分割与面向对象的水体信息提取[J].地理空间信息,2017,15(5):57-59.
[3]柯丽娟,赵红莉,蒋云钟.基于GF-1卫星遥感影像提取水体的方法比较[J].甘肃科技,2015(10):18-21.
[4]赵慧童,王萍,张娜,等.基于GF-1遥感数据的城市水体信息提取方法研究[J].测绘与空间地理信息,2017,40(10):70-73.
[5]GUO Q D,PU R L,LI J D,et al.A weighted normalized difference water index for water extraction using Landsat imagery[J].International Journal of Remote Sensing,2017,38(19):5430-5445.
[6]WANG X,XIE S,ZHANG X,et al.A robust multi-band water index(MBWI)for automated extraction of surface water from land sat 8 OLI imagery[J].International Journal of Applied Earth Observation and Geoinformation,2018,68:73-91.
[7]周小莉,郭加伟,刘锟铭.基于陆地成像仪影像和主成分分析的水体信息提取--以鄱阳湖区为例[J].激光与光电子学进展,2016(8):77-84.
[8]齐庆超,张小磊,金江峰,等.基于光谱角匹配算法的水体信息提取研究[J].测绘与空间地理信息,2016,39(2):93-96.
[9]赵紫薇.基于OSTU算法利用新型水体指数进行Landsat数据自适应阈值水体自动提取研究[J].测绘与空间地理信息,2016(9):57-60.
[10]MENG X B,LIU Y,GAO X Z,et al.A new bio-inspired algorithm:Chicken swarm optimization[C]//International Conference in Swarm Intelligence.Berlin:Springer,2014:86-94.
[11]孔飞,吴定会.一种改进的鸡群算法[J].江南大学学报:自然科学版,2015,14(6):681-688.
[12]张伟,赵理君,郑柯,等.一种改进光谱角匹配的水体信息提取方法[J].测绘通报,2017(10):34-38.
[13]袁欣智,江洪,陈芸芝,等.一种应用大津法的自适应阈值水体提取方法[J].遥感信息,2016,31(5):36-42.