An End-to-End Hyperspectral Image Classification Method Using Deep Convolutional Neural Network With Spatial Constraint

Hyperspectral image (HSI) classification is of vital importance in remote sensing-related applications. Various approaches, including the recently popular convolutional neural network (CNN)-based models, are proposed to tackle the problem of exploitation of the spatial and spectral features in the HSIs for the use of training classifier. In this letter, we design a simple but innovative end-to-end deep U-net-based model for HSI classification task. Unlike the previous CNN based models that mainly use CNN for spatial feature extraction and process the HSI data locally in small patches, our model takes the whole HSI as network input directly and outputs the predicted classes corresponding to each pixel location. Classification loss in the train data set and spatial constraint loss for the predicted result are combined as the loss function in the training stage to learn the mapping from HSI data to classification map and enhance the spatial continuity and consistency of the predicted result. Benchmark HSI data sets are used to evaluate the performance of the proposed method. Experimental results show that our model can achieve promising results comparing with the existing CNN-based methods.

Automated summary

This letter proposes a simple but innovative end-to-end deep U-net-based model for hyperspectral image classification, which directly takes the whole HSI as network input and outputs predicted classes for each pixel location. The combination of classification loss and spatial constraint loss in the training stage enhances the spatial continuity and consistency of the predicted results, showing promising performance compared to existing CNN-based methods.