多源遥感影像湿地检测概率潜在语义分析

Wetland Detection from Multi-sources Remote Sensing Images Based on Probabilistic Latent Semantic Analysis

提出了一种基于概率潜在语义分析的多源遥感影像湿地检测方法。首先提取高分辨率影像的光谱、纹理和湿地场景的地物组成成分,并结合由多光谱遥感数据提取的湿地地表温度、土壤含水量,组成湿地场景的特征空间;然后利用概率潜在语义分析将湿地场景表示成多个潜在语义的组合,并用潜在语义的权值向量来描述湿地场景的特征空间;最后利用SVM分类器实现湿地场景的检测。试验表明,概率潜在语义分析能够将湿地的高维特征空间映射到低维的潜在语义空间中,地物组成成分和定量环境特征的加入能更加有效地表征湿地特征空间,提高湿地检测精度。

A novel wetland detection approach for multi-sources remote sensing images was proposed, which based on the probabilistic latent semantic analysis （pLSA）. Firstly, spectral, texture, and subclass of wetland were extracted from high-resolution remote sensing image, and land surface temperature and soil moisture of wetland were derived from corresponding multispectral remote sensing image. The feature space of wetland scene was hence formed. Then, wetland scene was represented as a combination of several latent semantics using pLSA, and the feature space of the wetland scene was further described by weight vector of latent semantics. Finally, supporting vector machine （SVM） classifier was applied to detect the wetland scene. Experiments indicated that the adoption of pLSA is able to map the high-dimensional feature space of wetland to low-dimensional latent semantic space. Besides, the addition of subclass and quantitative environment features is able to characterize wetland feature space more effectively and improve the detection accuracy significantly.