Spatial Context based Angular Information Preserving Projection for Hyperspectral Image Classification
This work addresses the problem of improving classification accuracy in hyperspectral imaging for remote sensing applications, but it is incremental as it builds on existing angular information methods by adding spatial context.
The paper tackled hyperspectral image classification by proposing an unsupervised dimensionality reduction method that preserves local angular information and incorporates spatial context, along with a sparse representation classifier optimized for spatial information, achieving robust classification performance on two real-world datasets.
Dimensionality reduction is a crucial preprocessing for hyperspectral data analysis - finding an appropriate subspace is often required for subsequent image classification. In recent work, we proposed supervised angular information based dimensionality reduction methods to find effective subspaces. Since unlabeled data are often more readily available compared to labeled data, we propose an unsupervised projection that finds a lower dimensional subspace where local angular information is preserved. To exploit spatial information from the hyperspectral images, we further extend our unsupervised projection to incorporate spatial contextual information around each pixel in the image. Additionally, we also propose a sparse representation based classifier which is optimized to exploit spatial information during classification - we hence assert that our proposed projection is particularly suitable for classifiers where local similarity and spatial context are both important. Experimental results with two real-world hyperspectral datasets demonstrate that our proposed methods provide a robust classification performance.