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Scarcity of labeled samples is the main obstacle for hyperspectral image classification tasks when labeling data is considerably costly and time-consuming in real-world scenarios. To alleviate any underfitting problem that may occur due to lack of training data, semisupervised classification frameworks explore the intrinsic information of unlabeled samples and bridge labeled and unlabeled data. In...
Sentiment analysis is the method of analyzing a piece of text for human sentiment or emotions. In this paper, we consider the sentence-level sentiment classification task. Recently an end-to-end convolutional neural network has been proposed to predict sentiment polarity directly. However, this approach extracts features only based on input word embedding sets, and fails to make full use of a word's...
Classification has been among the central issues of hyperspectral application. However, due to the well-known Hughes phenomenon, most of the methods suffer from the curse of dimensionality and deeply rely on traditional dimensional reduction like Principle Component Analysis (PCA). In this paper, combining spatial and spectral information jointly, we propose a novel deep classification framework....
To combat the well-known Hughes phenomenon occurred in hyperspectral classification, most of the previous works adopt dimensionality reduction or manifold learning technique before supervised learning. While in this paper, we propose a different scheme: First, we design a pixel-wise classifier based on Convolutional Neural Network that could directly mapping observed spectrum to class distribution...
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