Abstract-We present an adaptation algorithm focused on the description of the data changes under different acquisition conditions. When considering a source and a destination domain, the adaptation is carried out by transforming one data set to the other using an appropriate nonlinear deformation. The eventually nonlinear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the samples in one domain are projected onto the other, thus allowing the application of any classifier or regressor in the transformed domain. Experiments on challenging remote sensing scenarios, such as multitemporal very high resolution image classification and angular effects compensation, show the validity of the proposed method to match-related domains and enhance the application of cross-domains image processing techniques. Index Terms-Domain adaptation, model portability, multitemporal classification, support vector machine (SVM), transfer learning.
An unsupervised feature transfer method based on feature space clustering and graph matching is proposed in REF .
Graph Matching for Adaptation in Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing
Computer Science