基于GF-2遥感影像的一种快速水体信息提取方法

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%。

A Fast Water Information Extraction Method Based on GF-2 Remote Sensing Image

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.