An active learning method based on SVM classifier for hyperspectral images classification

Sun, Shujin; Zhong, Ping; Xiao, Huaibao; Liu, Fang; Wang, Runsheng

doi:10.1109/whispers.2015.8075484

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…In the earlier days, HSI classification was done by the machine learning methods such as support vector machines (SVM) [ 3 , 4 ], k-nearest neighbor (KNN) [ 5 , 6 ], multinomial logistic regression (MLR) [ 7 , 8 ], and decision tree [ 9 , 10 ]. Within the similar data which exists, spectral changes in various materials and various spaces might have the same features, so the attained details were still corrupt because of inadequate spatial structure feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Reddy¹,

Harikiran²,

Enduri

et al. 2022

Computational Intelligence and Neuroscience

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The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary for improved classification accuracy. In the classification of hyperspectral images, deep learning has gained significant traction. This research analyzes how to accurately classify new HSI from limited samples with labels. A novel deep-learning-based categorization based on feature extraction and classification is designed for this purpose. Initial extraction of spectral and spatial information is followed by spectral and spatial information integration to generate fused features. The classification challenge is completed using a compressed synergic deep convolution neural network with Aquila optimization (CSDCNN-AO) model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO). The HSI, the Kennedy Space Center (KSC), the Indian Pines (IP) dataset, the Houston U (HU) dataset, and the Salinas Scene (SS) dataset are used for experiment assessment. The sequence testing on these four HSI-classified datasets demonstrate that our innovative framework outperforms the conventional technique on common evaluation measures such as average accuracy (AA), overall accuracy (OA), and Kappa coefficient (k). In addition, it significantly reduces training time and computational cost, resulting in enhanced training stability, maximum performance, and remarkable training accuracy.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Reddy¹,

Harikiran²,

Enduri

et al. 2022

Computational Intelligence and Neuroscience

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“…In the early age of hyperspectral image classification, traditional machine learning methods were widely used, for example, support vector machines (SVM) [ 9 , 10 ], k-nearest neighbor (KNN) [ 11 , 12 ], multinomial logistic regression (MLR) [ 13 , 14 ], decision tree [ 15 , 16 ]. However, within the same material exist spectral differences in different spaces and different materials may have similar spectral characteristics, so the obtained maps are still noisy due to the limited ability of spatial structure feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification

Qiu

Cao

et al. 2021

Micromachines

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The convolutional neural network (CNN) has been proven to have better performance in hyperspectral image (HSI) classification than traditional methods. Traditional CNN on hyperspectral image classification is used to pay more attention to spectral features and ignore spatial information. In this paper, a new HSI model called local and hybrid dilated convolution fusion network (LDFN) was proposed, which fuses the local information of details and rich spatial features by expanding the perception field. The details of our local and hybrid dilated convolution fusion network methods are as follows. First, many operations are selected, such as standard convolution, average pooling, dropout and batch normalization. Then, fusion operations of local and hybrid dilated convolution are included to extract rich spatial-spectral information. Last, different convolution layers are gathered into residual fusion networks and finally input into the softmax layer to classify. Three widely hyperspectral datasets (i.e., Salinas, Pavia University and Indian Pines) have been used in the experiments, which show that LDFN outperforms state-of-art classifiers.

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“…HSI classification is one of the most important steps in HSI analysis and application, the basic task of which is to determine a unique category for each pixel. In early research, the working mode of feature extraction combined with classifiers such as support vector machines (SVM) [1] and random forest (RF) [2] was dominant at the time. Initially, in order to alleviate the Hughes phenomenon caused by band redundancy, researchers introduced a series of feature extraction methods to extract spectral features conducive to classification from abundant spectral information.…”

Section: Introductionmentioning

confidence: 99%

Deep Relation Network for Hyperspectral Image Few-Shot Classification

et al. 2020

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Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial–spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models.

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An active learning method based on SVM classifier for hyperspectral images classification

Cited by 8 publications

References 8 publications

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification

Deep Relation Network for Hyperspectral Image Few-Shot Classification

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