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In this paper, a novel multiple-kernel learning (MKL) algorithm is proposed for classification of hyperspectral images. The goal of classification is to acquire the class label of each pixel. The land covers is linearly separable in the kernel space spanned by class labels (ideal kernel). The ideal kernel is used as the optimization objective of our proposed MKL algorithm. Linear programming (LP)...
Collaborative representation-based classification with distance-weighted Tikhonov regularization (CRT) has offered high accuracy and efficiency. Due to its per-pixel classification nature without a training step, this paper develops a parallel implementation by using compute unified device architecture (CUDA) on graphics processing units (GPUs). To further improve classification accuracy, local binary...
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...
In the process of hyperspectral image classification, the number of training samples is the key problem in improvement of classification performance. However, finding training samples are generally difficult and time-consuming. In this paper, we propose a novel semi-supervised approach and attempt to utilize unlabeled samples to improve classification accuracy. Specifically, active learning (AL) and...
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