Deep Manifold Embedding for Hyperspectral Image Classification

TL;DR

This paper introduces a deep manifold embedding method (DMEM) that models hyperspectral image data as nonlinear manifolds, capturing intrinsic structures for improved classification performance.

Contribution

It develops a novel approach that models data as nonlinear manifolds and preserves geodesic distances, enhancing hyperspectral image classification accuracy.

Findings

Effective on three real-world datasets

Outperforms traditional deep learning methods

Captures intrinsic data structures

Abstract

Deep learning methods have played a more and more important role in hyperspectral image classification. However, the general deep learning methods mainly take advantage of the information of sample itself or the pairwise information between samples while ignore the intrinsic data structure within the whole data. To tackle this problem, this work develops a novel deep manifold embedding method(DMEM) for hyperspectral image classification. First, each class in the image is modelled as a specific nonlinear manifold and the geodesic distance is used to measure the correlation between the samples. Then, based on the hierarchical clustering, the manifold structure of the data can be captured and each nonlinear data manifold can be divided into several sub-classes. Finally, considering the distribution of each sub-class and the correlation between different subclasses, the DMEM is constructed to preserve the estimated geodesic distances on the data manifold between the learned low dimensional features of different samples. Experiments over three real-world hyperspectral image datasets have demonstrated the effectiveness of the proposed method.