An Iterative Training Sample Updating Approach for Domain Adaptation in Hyperspectral Image Classification

Acquiring training samples in remote sensing images is always expensive and time-consuming. As a consequence, it would be preferable if one domain without training samples (the target domain) could be classified given a priori knowledge from another domain (the source domain). In this letter, an iterative training sample updating (ITSU) approach is proposed based on a posteriori spatial feature extraction. First, the classifier is trained with initial training samples from the source domain and applied to the target domain, producing a preclassification map. Then, as an invariant feature, the a posteriori spatial features are extracted with a guided filter. Based on the spectral features and the a posteriori spatial features, a criterion measuring the similarity of the cross-domain samples is defined. New training samples from the target domain are assigned with pseudo-labels, and the original samples in the source domain are removed. Furthermore, the a posteriori spatial feature maps are fed back to the input images, and new classifiers are trained with an updated training sample set in the updated feature space. This procedure is repeated until the stopping rule is satisfied. Finally, the adapted classifier is obtained based on the updated training samples. The experimental results on three hyperspectral data sets indicated that ITSU achieved the best performance compared with the other two state-of-the-art methods.

Automated summary

The proposed ITSU approach aims to classify target domains without training samples by transferring knowledge from source domains, based on iterative training sample updating and a posteriori spatial feature extraction. The method trains classifiers with initial samples, extracts a posteriori features, defines similarity criteria, updates samples, and repeats until convergence, achieving superior performance in experimental results.