عنوان انگلیسی مقاله:
A deep manifold learning approach for spatial-spectral classification with limited labeled training samples
ترجمه فارسی عنوان مقاله:
یک رویکرد یادگیری منیفولد عمیق برای طبقه بندی مکانی طیفی با نمونه های آموزش دارای برچسب محدود
Sciencedirect - Elsevier - Neurocomputing, 331 (2019) 138-149: doi:10:1016/j:neucom:2018:11:047
Xichuan Zhou a , b , ∗, Nian Liu b , Fang Tang b , Yingjun Zhao c , Kai Qin c , Lei Zhang b , Dong Li b
One major challenge of designing deep learning systems for hyperspectral data classification is the lack of labeled training samples. Inspired by recent manifold learning researches, this paper presents a novel Lo- cality Preserving Convolutional Network to address this challenge. The proposed method invents a semi- supervised locality-preserving regularization operation, and inserts a new layer in the three-dimensional convolutional neural network for end-to-end spatial-spectral classification. The benefits are three-fold. First, by using unlabeled training samples which are more easily available, the proposed method re- duces the number of labeled samples required for training a deep learning model; Second, the proposed method incorporates the intrinsic geographical correlation among nearby samples into the extracted fea- tures, which prevents it from losing accuracy when only limited labeled samples are available; Third, with a three-dimensional architecture, the proposed method can extract the spatial and spectral features simultaneously from the hyperspectral data for classification. A gradient-decent based approach is de- ployed to train the whole network in a unified way. Experiments over different benchmarks show that, the proposed method relieves the Hughes phenomenon for deep learning, and achieves competitively high classification accuracy compared to other state-of-the-art approaches.
Keywords: Deep learning | Hyperspectral images | Limited labeled samples | Locality preserving convolutional network