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In this work, we develop a new framework to combine ensemble learning and composite kernel learning for hyperspectral image classification. We refer it as the multiple composite kernel learning, which is based on an iterative architecture. More specifically, in each iteration, we use the rotation-based ensemble to create rotation matrix, which is used to generate rotated features for both spectral...
The increase in spatial and spectral resolution of the satellite sensors has provided high-quality data for remote sensing image classification. However, the high-dimensional feature space induced by using many spatial information precludes the use of simple classifiers. This paper proposes to classify the hyperspectral images and simultaneously to learn significant features in such high-dimensional...
Performance of hyperspectral image classification depends on feature extraction. Compared with conventional hand-crafted feature extraction, deep learning can learn feature with more discriminative information. In this paper, a two-channel deep convolutional neural network (Two-CNN) is proposed to learn jointly spectral-spatial feature from hyperspectral image. The proposed model is composed of two...
In this paper, a novel feature extraction framework is proposed for hyperspectral image classification. Inspired by the role of discriminant function in classifier, which intends to learn a mapping from the input features to label information in class space, we develop a feature extraction framework to learn the new feature representation of original input features in class space, by establishing...
Hyperspectral data classification problems have been extensively studied in the past decade. However, well designed features and a robust classifier are still open issues that impact on the performance of an automatic land-cover classification system. In this paper, we propose a deep feature represenation method that generates very good features and a classifier for pixel-wise hyperspectral data classification...
For classification of hyperspectral images, particularly using limited training samples, supervised feature extraction is an approach for reduction of dimensionality, overcoming the Hughes phenomenon and increasing the classification accuracies. Classic and popular feature extraction methods such as linear discriminant analysis (LDA) have not good efficiency in small sample size situation because...
A multiclass support tensor machine (STM) for the classification of remotely-sensed imagery is investigated in this study aiming at simultaneously exploiting the spectral and spatial information for accurate image interpretation. Spatial relationship of neighboring pixels has been taken into consideration by a local pixel neighborhood (LPN), which processes the local imagery patch as a cube, and is...
Classification of hyperspectral remote sensing images is affected by two main problems: high dimensionality of the acquired signatures and scarce availability of labeled samples. Learning a low dimensional manifold and active learning represent two approaches that have been investigated in the literature to mitigate these effects. However they are usually applied independently from each other. In...
The support vector machine (SVM) was a new machine learning technique developed on the basis of statistical learning theory. It is the most successful realization of statistical learning theory. To testify the validity of SVM, this study chose the data set of hyperspectral images sensed by AVIRIS, with the band selected by Bhattacharya distance. And it added different scales of texture information...
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