Kernel change discriminant analysis for multitemporal cloud masking

This paper presents a multitemporal feature extraction method based on kernels that is particularly designed for change detection. The method provides features that maximize specific changes between two dates while minimizing sources of errors, such as residual land-cover changes and misregistration errors, in the time series. The extracted features computed in the kernel feature space can deal with non-linear relations between samples at different dates. Moreover, no supervised information is required to find the changes of interest for the selected dates in the time series. The effectiveness of the proposed method is successfully illustrated in a cloud masking application using a Landsat time series. Results show that the proposed method provides the most discriminative features in terms of cloud detection when confronted with state of the art linear and nonlinear unsupervised feature extraction algorithms. In particular, extracted features with the proposed method enable automatic cloud detection in multispectral time series.