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Active learning has obtained a great success in supervised remotely sensed hyperspectral image classification, since it can be used to select highly informative training samples. As an intrinsically biased sampling approach, it generally favors the selection of samples following discriminative distributions, i.e., those located in low density areas in feature space. However, the hyperspectral data...
In this paper, we joint autoencoder with active learning for hyperspectral imagery classification. Specifically, we learn the classifier via autoencoder, where the most informative samples are acitvely selected through the interaction between the autoencoder and active learning. Experimental results, conducted using both the Kennedy Space Center and the Indian Pines hyperspectral images, show that...
Spectral unmixing is a fast growing area in hyperspectral image analysis. Many algorithms have been recently developed to retrieve pure spectral components (endmembers) and determine their abundance fractions in mixed pixels, which dominate hyperspectral images. However, possible connections between spectral unmixing concepts and classification algorithms have been rarely investigated. In this work,...
This paper introduces a new supervised Bayesian approach to hyper-spectral image segmentation. The algorithm mainly consists of two steps: (a) learning, for each class label, the posterior probability distributions, based on a multinomial logistic regression model; (b) segmenting the hyperspectral image, based on the posterior probability distribution of the image of class labels built on the learned...
This paper presents a new semisupervised segmentation algorithm, suited to high-dimensional data, of which remotely sensed hyperspectral image data sets are an example. The algorithm implements two main steps: 1) semisupervised learning of the posterior class distributions followed by 2) segmentation, which infers an image of class labels from a posterior distribution built on the learned class distributions...
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