Domain Adaptation Support Tensor Machine: An Extended STM for Object Recognition Using Cross-Source Heterogeneous Remote Sensing Data

Multisource remote sensing data observed from sensors with different resolutions and physical properties will present heterogeneous tensor structures and diverse feature distributions, thus posing a significant challenge for building an effective classifier for cross-source object recognition. The representative support tensor machine classifier can inherently preserve tensor structure information of remote sensing data and obtain effective recognition ability, while it can only handle same-source and same-distributed homogeneous data and fail to deal with cross-source heterogeneous remote sensing data with complex structures and various distributions. Therefore, the domain adaptation support tensor machine (DA-STM) is proposed to learn a uniform model for cross-source object recognition. To process heterogeneous tensor from different sources, multiple factor matrices with different modes are constructed to eliminate the structural differences and reduce distribution discrepancies for multisource heterogeneous data. To excavate shared classification information across sources, the shared core tensor is established to learn the classification hyperplane jointly using multisource data, and the adaptive sample labels are then embedded into the model to recover the class information during model training. To ensure efficient training, the decomposition algorithm is developed to accelerate the solving of dual problem of DA-STM. In addition, to improve classification performance as the acquirement of sequential samples, the proposed DA-STM is further upgraded to an online version to update the classification parameters dynamically. Using multi-resolution and multi-angle optical images as well as multi-angle SAR images, experimental results demonstrate that the proposed DA-STM can obtain better recognition results than typical domain adaptation methods.