Time Correlation Entropy: A Novel Multitemporal PolSAR Feature and Its Application in Salt Lake Classification

Multitemporal polarimetric synthetic aperture radar (PolSAR) data capture the temporal variations in polarization parameters, enabling more accurate land cover classification. Most existing multitemporal PolSAR features rely on comparing only two-time points. These approaches can be limited in capturing cumulative changes over a longer period. To better represent the cumulative changes of land cover in the entire time-series, this article proposes a multitemporal PolSAR feature, namely time correlation entropy. We first extract the dominant scattering mechanism of targets from the polarization covariance matrices using matrix decomposition. Then the time correlation matrix is constructed by comparing all dominant scattering mechanism pairs in the time series. From the Shannon entropy, the entropy of the time correlation matrix, that is, time correlation entropy, is derived to indicate the degree of changes in the land cover during the observation period. Finally, the maximum entropy principle is further applied to prove that this entropy conforms to a normal distribution. Following this corollary, a classification method based on the interval estimation of distribution parameters is proposed. We evaluate the proposed feature and classification on the salt lake classification application in Qarhan Salt Lake and Gasikule Salt Lake using Sentinel-1 images. Compared to common PolSAR features and classification methods, our method gains the best results. Besides, its results also have better regional consistency and noise resistance.