Spectral and Spatial Proximity-Based Manifold Alignment for Multitemporal Hyperspectral Image Classification

Multitemporal hyperspectral images provide valuable information for a wide range of applications related to supervised classification, including long-term environmental monitoring and land cover change detection. However, the required ground reference data are time-consuming and expensive to acquire, motivating researchers to investigate options for reusing limited training data for classification of other temporal images. Current studies that address high dimensionality and non-stationarity inherent in temporal hyperspectral data for classification are limited for the case where significant spectral drift exists between images. In this paper, we adapt and extend two manifold alignment (MA) methods for classification of multitemporal hyperspectral images in a common manifold space, assuming that the local geometries of two temporal spectral images are similar. The first method exploits a locally based manifold configuration of a source image (considered to be the prior manifold), and the second approach links local manifolds of two images using bridging pairs. In addition to exploiting manifolds estimated with spectral information for MA, we also demonstrate how spatial information can be incorporated into the MA methods. When evaluated using three Hyperion data sets, the proposed methods outperform four baseline approaches and two state-of-the-art domain adaptation methods. The advantages of the proposed MA methods are more evident when significant spectral drift exists between two temporal images. In addition to the promising classification results, the proposed methods establish a domain adaptation framework for analysis of temporal hyperspectral data based on data geometry.