Abstract
A new methodology, the hybrid learning system (HLS), based upon semi-supervised learning is proposed. HLS categorizes hyperspectral images into segmented regions with discriminative features using reduced training size. The technique utilizes the modified breaking ties (MBT) algorithm for active learning and unsupervised learning-based regressors, viz. multinomial logistic regression, for hyperspectral image categorization. The probabilities estimated by multinomial logistic regression for each sample helps towards improved segregation. The high dimensionality leads to a curse of dimensionality, which ultimately deteriorates the performance of remote sensing data classification, and the problem aggravates further if labeled training samples are limited. Many studies have tried to address the problem and have employed different methodologies for remote sensing data classification, such as kernelized methods, because of insensitiveness towards the utilization of large dataset information and active learning (AL) approaches (breaking ties as a representative) to choose only prominent samples for training data. The HLS methodology proposed in the current study is a combination of supervised and unsupervised training with generalized composite kernels generating posterior class probabilities for classification. In order to retrieve the best segmentation labels, we employed Markov random fields, which make use of prior labels from the output of the multinomial logistic regression. The comparison of HLS was carried out with known methodologies, using benchmark hyperspectral imaging (HI) datasets, namely “Indian Pines” and “Pavia University”. Findings of this study show that the HLS yields the overall accuracy of {99.93% and 99.98%}Indian Pines and {99.14% and 99.42%}Pavia University for classification and segmentation, respectively.
Highlights
Hyperspectral imaging (HI), introduced at National Aeronautics and Space Administration (NASA)’s Jet Propulsion Laboratory [1], is a system that consists of spatial in conjunction with spectral coordinates forming a hyperspectral cube
The modified breaking ties (MBT) algorithm was applied for active learning (AL) and multinomial logistic regression is applied for unlabeled hyperspectral imaging (HI) data
The attribute filters (AFs) for computing attribute profiles (APs), using gray level images were defined in a mathematical morphological framework that operated by combining components at different threshold levels
Summary
Hyperspectral imaging (HI), introduced at National Aeronautics and Space Administration (NASA)’s Jet Propulsion Laboratory [1], is a system that consists of spatial in conjunction with spectral coordinates forming a hyperspectral cube. There exist numerous methods for the use of large ground truth information such as SVM [39], semisupervised SVM [40], multinomial logistic regression (MLR) [41], partial least squares regression [42], and artificial neural networks [43] In both spectral and spatial information, researchers have started to employ neural networks such as densely connected multiscale attention networks [44], end-to-end fully convolutional segmentation networks [45], and convolutional neural networks (CNN) with random forest [46] and Ghostnet [47]. A hybrid learning system (HLS) is introduced and tested that categorizes hyperspectral images into segmented regions with discriminative features using reduced training size It is based on a semi-supervised learning approach, using an MBT algorithm for AL and multinomial logistic regression for unlabeled HI data categorization. The article is organized as follows: Section 2 describes the Methodology, including dataset description with processes and stages involved in HI; Results and Discussion are reported in Section 3 with a comprehensive comparison with existing researchers’ work, and Section 4 provides the concluding remarks
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