Multitemporal image classification and change detection with kernels

We explicitly formulate a family of kernel-based methods for (supervised and partially supervised) multitemporal classification and change detection. The novel composite kernels developed account for the static and temporal cross-information between pixels of subsequent images simultaneously. The methodology also takes into account spectral, spatial, and temporal information, and contains the familiar difference and ratioing methods in the kernel space as a particular cases. The methodology also permits straightforward fusion of multisource information. Several scenarios are considered in which partial or complete labeled information at the prediction time is available. The developed methods are then tested under different classification frameworks: (1) inductive support vector machines (SVM), and (2) one-class support vector data description (SVDD) classifier, in which only samples of a class of interest are used for training. The proposed methods are tested in a challenging real problem for urban monitoring. The composite kernel approach is additionally used as a fusion methodology to combine synthetic aperture radar (SAR) and multispectral data, and to integrate the spatial and textural information at different scales and orientations through Gabor filters. Good results are observed in almost all scenarios; the SVDD classifier demonstrates robust multitemporal classification and adaptation capabilities when few labeled information is available, and SVMs show improved performance in the change detection approach.