Abstract
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.
Highlights
As for the demand of large-scale and high-efficiency crop mapping, remote sensing technology can substitute for the traditional field measurement and it can observe the same area many times in a short revisit time
The reason is that the vegetation mostly grows in undulated mountains, where the speckle noise is stronger in polarimetric synthetic aperture radar (PolSAR) images and reduce the classification accuracy
The GF-3 PolSAR data is sensitive to the change of morphological structure during crop growth, whereas the Sentinel-2A optical data can show the change of moisture and chlorophyll content in whereas the Sentinel-2A optical data can show the change of moisture and chlorophyll content in crop leaves well
Summary
As for the demand of large-scale and high-efficiency crop mapping, remote sensing technology can substitute for the traditional field measurement and it can observe the same area many times in a short revisit time. In the integrated classification, some effective features extracted from data of different sensors cannot be used at the same time, so that the potential of integrated datasets cannot be fully explored. The covariance matrix of PolSAR data is difficult to be combined with multi-spectral optical data for classification. Such intensity vector has nine bands, denoting the intensity values on different polarization bases, which has the similar data structure with the spectral bands of optical data, so it is easy to combine these two kinds of information. Some other useful features are extracted, including the polarimetric features, as the radar vegetation index (RVI) and the decomposed Yamguichi four components, as well as some optical features as the Sensors 2018, 18, 3139; doi:10.3390/s18093139 www.mdpi.com/journal/sensors
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