Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image Classification
This work addresses the scarcity of labeled data in hyperspectral image classification, an incremental improvement for remote sensing applications.
The authors tackled the problem of limited labeled samples in hyperspectral image classification by proposing a multitask deep learning method that trains a shared feature extractor across multiple datasets, achieving higher classification accuracies on three datasets (Pavia University, Pavia Center, and Indian Pines) and competitive results on Salinas Valley compared to baselines.
In this letter, we propose a multitask deep learning method for classification of multiple hyperspectral data in a single training. Deep learning models have achieved promising results on hyperspectral image classification, but their performance highly rely on sufficient labeled samples, which are scarce on hyperspectral images. However, samples from multiple data sets might be sufficient to train one deep learning model, thereby improving its performance. To do so, we trained an identical feature extractor for all data, and the extracted features were fed into corresponding Softmax classifiers. Spectral knowledge was introduced to ensure that the shared features were similar across domains. Four hyperspectral data sets were used in the experiments. We achieved higher classification accuracies on three data sets (Pavia University, Pavia Center, and Indian Pines) and competitive results on the Salinas Valley data compared with the baseline. Spectral knowledge was useful to prevent the deep network from overfitting when the data shared similar spectral response. The proposed method tested on two deep CNNs successfully shows its ability to utilize samples from multiple data sets and enhance networks' performance.