A 3-Stage Spectral-Spatial Method for Hyperspectral Image Classification

Hyperspectral images often have hundreds of spectral bands of different wavelengths captured by aircraft or satellites that record land coverage. Identifying detailed classes of pixels becomes feasible due to the enhancement in spectral and spatial resolution of hyperspectral images. In this work, we propose a novel framework that utilizes both spatial and spectral information for classifying pixels in hyperspectral images. The method consists of three stages. In the first stage, the pre-processing stage, the Nested Sliding Window algorithm is used to reconstruct the original data by enhancing the consistency of neighboring pixels and then Principal Component Analysis is used to reduce the dimension of data. In the second stage, Support Vector Machines are trained to estimate the pixel-wise probability map of each class using the spectral information from the images. Finally, a smoothed total variation model is applied to ensure spatial connectivity in the classification map by smoothing the class probability tensor. We demonstrate the superiority of our method against three state-of-the-art algorithms on six benchmark hyperspectral datasets with 10 to 50 training labels for each class. The results show that our method gives the overall best performance in accuracy even with a very small set of labeled pixels. Especially, the gain in accuracy with respect to other state-of-the-art algorithms increases when the number of labeled pixels decreases, and, therefore, our method is more advantageous to be applied to problems with a small training set. Hence, it is of great practical significance since expert annotations are often expensive and difficult to collect.