Feature Extraction and Classification of Hyperspectral Images Using Hierarchical Network

In recent years, researchers have frequently utilized convolutional neural networks (CNNs) to classify hyperspectral images and have, indeed, embraced exciting achievements. However, most of the existing approaches tend to handle images block by block, which is less efficient as image blocks need to be fed into the network for many times. With this in mind, this letter presents a novel hierarchical CNN that adopts raw images as the input and extracts useful features for classification. Specifically, we adopt several hierarchical convolutional neural layers as a feature extractor and adopt the support vector machine instead of the classifying layer in the original network as the final classifier. Experiments show the proposed approach can work efficiently and exhibit competitive performance when compared to some other approaches based on deep networks.