Semi-Supervised Hyperspectral Image Classification via Spatial-Regulated Self-Training

Wu, Yue; Mu, Guifeng; Qin, Can; Miao, Qiguang; Ma, Wenping; Zhang, Xiangrong

doi:10.3390/rs12010159

Cited by 78 publications

(32 citation statements)

References 51 publications

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“…There are many studies in literature on multispectral and hyperspectral images [1][2][3]. Seniha et al proposed two methods for extracting the canopy area of the LiDAR sensor from hyperspectral data [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Image processing and machine learning‐based classification method for hyperspectral images

Yaman

Yetiş

Karaköse

2021

The Journal of Engineering

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Hyperspectral images are used to produce effective solutions in many areas due to the number of information they have. However, the number of information they provide may not bring an advantage always, in contrast, it may cause confusion sometimes. In this study, we propose a machine learning-based classification method on the reduced bands using median and mean filters. The proposed method is tested on Indian Pines, Pavia University and Salinas datasets. After applying normalization, median filter and mathematical morphology for each band in an image, a feature matrix with only one band is achieved. Then, the most significant features are selected by using relief feature selection algorithm. The selected features are used for classification with support vector machine and K nearest neighbourhood. For all methods and datasets, a success rate above 99% in terms of accuracy is achieved. The proposed method has two significant contributions. First, when the proposed method is compared with the similar studies in the literature, it is clearly seen that the features selected by relief algorithm significantly increase the success rate of classification algorithms. Second significant contribution of using relief algorithm is obtaining faster methods by reducing the number of hyperspectral bands. How to cite this article: Yaman O, Yetiş H, Karaköse M. Image processing and machine learning-based classification method for hyperspectral images.

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Section: Literature Reviewmentioning

confidence: 99%

Image processing and machine learning‐based classification method for hyperspectral images

Yaman

Yetiş

Karaköse

2021

The Journal of Engineering

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“…In order to improve classification accuracy under the condition of limited labeled samples, semisupervised learning and data augmentation are widely applied. In [35,36], CNN was combined with semisupervised classification. In [37], Kang et al first extracted PCA, EMP, and edge-preserving features (EPF), then carried out classification by combining semisupervised method and decision confusion strategy.…”

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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“…Third, training the CNN with limited samples is challenging, because the usually complex CNN models are prone to an overfitting problem given a small number of training samples. To address the limited samples issue, some semisupervised methods have been proposed [36]- [40]. Semisupervised learning aims to address the issue by leveraging many unlabeled samples through identifying some unlabeled pixels and uses them for re-training the algorithm in an iterative manner.…”

Section: Introductmentioning

confidence: 99%

A Depthwise Separable Fully Convolutional ResNet With ConvCRF for Semisupervised Hyperspectral Image Classification

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral images (HSIs) classification relies on the accurate and efficient extraction of discriminative features, detail preservation, and efficient learning with limited training samples. This paper, therefore, presents an advanced neural network architecture combined with convolutional conditional random fields (ConvCRF) and region growing (RGW) approaches to address these key issues. First, a depthwise separable fully convolutional residual network (DFRes) is proposed for efficient feature learning, where a fully convolutional operation ensures a larger field of view, and residual learning and depthwise separable convolution can mitigate the problem of vanishing gradient and overfitting. Second, because the collection of ground-truth labels is usually difficult, the proposed architecture integrates the RGW method to effectively overcome the problem of limited training samples. Third, ConvCRF is used to preserve the image details for fine-grained predictions. Finally, the above key components are coherently integrated into the new semisupervised framework, i.e., DFRes with Conditional random fields and Region growing (DFRes-CR). Experimental results on three hyperspectral datasets demonstrate that the proposed approach outperforms other state-of-the-art methods.

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Semi-Supervised Hyperspectral Image Classification via Spatial-Regulated Self-Training

Cited by 78 publications

References 51 publications

Image processing and machine learning‐based classification method for hyperspectral images

Image processing and machine learning‐based classification method for hyperspectral images

Deep Relation Network for Hyperspectral Image Few-Shot Classification

A Depthwise Separable Fully Convolutional ResNet With ConvCRF for Semisupervised Hyperspectral Image Classification

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