Research on hyper-spectral remote sensing image classification by applying stacked de-noising auto-encoders neural network

Abstract

Hyper-spectral image can provide precise information on land surface targets identification and classification thanks to its advanced feature on spectral resolution. However, due to its complicated hyper-dimension data structure, greater challenge is put on the conventional image classification methods for hyper-spectral images. To fill this technical knowledge gap, we introduce a deep learning-based feature extraction method for hyper-spectral data classification. Firstly, we used a Stacked De-noising Auto-encoders(SDAE) to extract the in-depth features of hyper-spectral image data: a large amount of unlabeled data was pre-trained to extract the depth characteristics of pixels. We added random noise into the input layer of the network to form a de-noising auto-encoder machine and added treated inputs to reconstruct original data. An L-BFGS (Limited-memory quasi-Newton code) was used to optimize the loss function. In the top layer, deep neural network was fine-tuned by a Softmax regression classifier. All these improvements directed towards the model to obtain the image element abstraction and robust expression in the classification task of the hyper-spectral images. We tested the model performance on a Hyspex imaging spectrometer image and found that the newly introduced model framework outperforms other traditional methods including principal component analysis (PCA), and support vector machine (SVM) classifier, combined PCA-SVM classifier, and Minimum Noise Fraction Rotation (MNF)-SVM classifier.