School of Geosciences and Info-Physics, Central South University, Changsha 410083, China.
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, China.
Sensors (Basel). 2018 Sep 17;18(9):3139. doi: 10.3390/s18093139.
With the increasing of satellite sensors, more available multi-source data can be used for large-scale high-precision crop classification. Both polarimetric synthetic aperture radar (PolSAR) and multi-spectral optical data have been widely used for classification. However, it is difficult to combine the covariance matrix of PolSAR data with the spectral bands of optical data. Using Hoekman's method, this study solves the above problems by transforming the covariance matrix to an intensity vector that includes multiple intensity values on different polarization basis. In order to reduce the features redundancy, the principal component analysis (PCA) algorithm is adopted to select some useful polarimetric and optical features. In this study, the PolSAR data acquired by satellite Gaofen-3 (GF-3) on 19 July 2017 and the optical data acquired by Sentinel-2A on 17 July 2017 over the Dongting lake basin are selected for the validation experiment. The results show that the full feature integration method proposed in this study achieves an overall classification accuracy of 85.27%, higher than that of the single dataset method or some other feature integration modes.
随着卫星传感器的增多，更多可用的多源数据可用于大规模高精度作物分类。极化合成孔径雷达 (PolSAR) 和多光谱光学数据都已被广泛用于分类。然而，将 PolSAR 数据的协方差矩阵与光学数据的光谱波段相结合是很困难的。本研究采用 Hoekman 方法，通过将协方差矩阵转换为包含不同偏振基上多个强度值的强度向量，解决了上述问题。为了减少特征冗余，采用主成分分析 (PCA) 算法选择一些有用的极化和光学特征。本研究选择了 2017 年 7 月 19 日 GF-3 卫星获取的 PolSAR 数据和 2017 年 7 月 17 日 Sentinel-2A 卫星获取的洞庭湖流域光学数据进行验证实验。结果表明，本研究提出的全特征融合方法的总体分类精度为 85.27%，高于单一数据集方法或其他一些特征融合模式。
