Segmentation-Aware Hyperspectral Image Classification
This improves classification accuracy for remote sensing applications, especially when training data is limited.
The paper tackles hyperspectral image classification by combining a deep residual network with segmentation-aware superpixels, achieving state-of-the-art results on two benchmark datasets.
In this paper, we propose an unified hyperspectral image classification method which takes three-dimensional hyperspectral data cube as an input and produces a classification map. In the proposed method, a deep neural network which uses spectral and spatial information together with residual connections, and pixel affinity network based segmentation-aware superpixels are used together. In the architecture, segmentation-aware superpixels run on the initial classification map of deep residual network, and apply majority voting on obtained results. Experimental results show that our propoped method yields state-of-the-art results in two benchmark datasets. Moreover, we also show that the segmentation-aware superpixels have great contribution to the success of hyperspectral image classification methods in cases where training data is insufficient.