A surface water body extraction method based on domestic remote sensing imagery of high resolution
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摘要: 遥感图像中地表水体同山体、建筑物等地物产生的阴影在光谱特征上存在较高的类间相似性，导致提取过程中容易出现混淆和错分的情况。针对此问题，提出一种基于面向对象和人工蜂群的地表水体提取方法。该方法首先对遥感图像进行分割以获取分割对象的光谱、比率、几何形状等统计特征，以弥补高分遥感图像波段数目少，信息量不足的缺陷；并借助人工蜂群算法在解决复杂问题最优化方面的优势，选取水体同阴影二值分类的几何平均正确率作为算法的适应度函数，最终获取地表水体的最优化提取规则。选取厦门市大嶝岛和湖南省资兴市部分区域，基于国产高分一号、二号遥感数据进行水体提取，并与传统SVM分类结果进行比较。实验结果表明本算法提取水体的总体精度和Kappa系数均优于传统SVM分类器，表明该方法可应用于高分遥感图像的地表水体提取。Abstract: Due to the high spectral similarity existing in water and shadow, extraction of remote sensing imagery is easily confused and misclassified. To address this problem, we propose a method combined with the object-oriented image segmentation and the artificial bee colony algorithm (ABC) to extract surface water body from remote sensing imagery. Firstly, a series of statistic factors, such as spectrum, ratio and sharp features, are calculated during image segmentation. We used these factors to make up the defect of insufficient information existing in high-resolution imagery. Then, with the strength of solving complicate problem by ABC algorithms, we chose the geometric mean of accuracies between surface water bodies and shadows as the fitness function of classifier to generate the optimal extraction rules. The experiments are carried out in the Dadeng island of Xiamen in Fujian and part of the city of Zixing in Hunan, which are based on the domestic GF-1 and GF-2 remote sensing imageries. The results are compared with the SVM classifier and show that the proposed method can achieve better overall accuracy and Kappa coefficient, indicating that the proposed method is suitable for extraction of surface water bodies from remote sensing imagery of high spatial resolution.
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Key words:
- artificial bee colony /
- water extraction /
- GF /
- object-oriented /
- remote sensing classification
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