Graph-Embedding Balanced Transfer Subspace Learning for Hyperspectral Cross-Scene Classification

Abstract

Hyperspectral cross-scene classification utilizes the prior knowledge of source scenes with known labels to classify unlabeled target scenes via transfer learning. The existing methods did not properly balance the contribution of marginal and conditional distribution to transfer learning. They did not fully exploit the neighborhood information of intraclass/interclass in the shared transfer subspace. Regarding the two problems, first, by using maximum mean discrepancy and class weights, a direct estimation method was proposed for the balance parameter that adjusted the contribution of the marginal and conditional distribution. It could avoid the inefficiency problem of the traditional indirect estimation method, without introducing extra parameters. Two different strategies (supervised and unsupervised) were developed to comprehensively represent the feature relationship. The difference between source data and the target data was significantly reduced. At the same time, the intraclass and interclass relationships, represented by graph construction in the original high-dimensional space under graph-embedding framework, were maximally maintained. Experimental results on two classical cross-scene data sets (HyRank and Pavia) showed that the proposed method performed more efficiently and accurately in estimating the balance parameter and achieved better cross-scene classification performance by integrating with the graph construction strategies.

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