基于融合视觉词袋的高分遥感水体提取算法

Water Body Extraction from High Resolution Remote Sensing Images Based on Fused Visual Word Bags

针对高分遥感影像中水体目标提取易受植被阴影及光照条件等影响问题,提出基于融合视觉词袋的高分遥感水体目标提取算法。在分析遥感水体特性基础上,提出一种遥感水体目标频谱特征提取方法。为增强水体目标特征表达能力,设计一种基于局部二值模式和频谱特征的融合视觉词袋模型。基于提出的模型,设计水体目标分类器。为进一步细化水体和非水体边界,提出对水陆交界区域进行二次识别,得到优化后的水体目标提取结果。实验结果表明:提出算法能有效检测高分遥感影像中水体目标,在准确率和Kappa系数上表现良好。

Aiming at the problem that water body extraction is easily influenced by shadow or light in high resolution remote sensing images,an improved algorithm based on fusion of visual word bags is proposed.Based on the deep analysis of the characteristics of remote sensing water body targets,a spectral feature extraction approach is designed.To enhance the description ability of water body targets,a novel visual word bag fusion model based on local binary pattern and spectral feature is constructed.Based on the proposed visual word bag fusion model,a water body target classifier is presented.To further classify the boundaries of the water body targets and the non-water body objects,an optimization algorithm is proposed and the final water body targets extraction results can be obtained.Experimental results show that the proposed algorithm can extract the water body targets exactly,and performs well in accuracy and Kappa coefficient.