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 successfully utilized samples from multiple data sets to increase its performance.
翻译:在这封信中,我们提出一种多任务深度学习方法,用于在一次培训中对多超光谱数据进行分类。深层学习模型在超光谱图像分类方面已经取得了大有希望的成果,但其性能高度依赖在超光谱图像上稀少的标签样本。然而,多个数据集的样本可能足以培训一个深层学习模型,从而改进其性能。为此,我们培训了所有数据的一个相同的特征提取器,并将提取的特征输入相应的软模子分类器。引入了光谱学知识,以确保共享的特征在各领域之间具有相似性。在实验中使用了四个超光谱数据集。我们在三个数据集(帕维亚大学、帕维亚中心和印度派恩斯)上实现了更高的分类,并在萨利纳斯河谷数据上取得了与基线相比的竞争性结果。光谱知识有助于防止深海网络在数据共享类似光谱响应时过度匹配。拟议方法成功地利用了多个数据集的样本来提高其性能。