Structure Preserved Discriminative Distribution Adaptation for Multihyperspectral Image Collaborative Classification

The fine spectra of the hyperspectral (HS) images can fully reflect the subtle features of the spectra of different objects. However, due to the limitation of the imaging equipment, its swath is not as large as that of multispectral (MS) images. The acquisition of MS images is more convenient, but the discrimination of spectral features is relatively poor. This paper aims to investigate how partially overlapping HS images can be utilized to improve the classification accuracy of large-scene MS images. Due to the spectral mismatch existing between MS and HS features, traditional transfer learning methods cannot solve the problem of classification with heterogeneous features. To address this issue, a novel structure-preserving discriminative distribution adaptive MS-HS image collaborative classification method is proposed in this paper, which aims to improve the classification accuracy of large-scene MS images by discriminative features. Specifically, this method combines statistical properties and geometric constraints in transfer learning, and jointly maximizes the distance between different classes by discriminative least squares to maximize classification accuracy. Moreover, the source and target domains are probabilistically adaptive while maintaining the local structure of MS-HS features, so that the data distribution is fully aligned and the distance between different classes is increased. The learned mapping matrix enables the mapping of multi-scale spectral-spatial features of MS-HS images to subspaces for classification. Compared with related advanced methods, three sets of MS-HS data sets show that the proposed method can effectively reduce the differences between MS-HS data and achieve better classification results.