Multi-Classification and Recognition of Hyperspectral Remote Sensing Objects Based on Convolutional Neural Network

Miao, 闫苗 Yan; Hongdong, 赵红东 Zhao; Yuhai, 李宇海 Li; Jie, 张洁 Zhang; Zetong, 赵泽通 Zhao

doi:10.3788/lop56.021702

Cited by 2 publications

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“…In the field of remote sensing, deep learning technology has attracted extensive attention from scholars, and many experts have utilized deep learning methods for tree species classification, achieving good classification results [10][11][12]. Particularly, convolutional neural networks (CNNs) have achieved significant success in computer vision tasks such as image classification, object detection, and semantic segmentation [13][14][15]. Due to their powerful feature extraction capabilities, CNNs have become the most commonly used neural networks in hyperspectral tree species classification [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid convolution neural network for the classification of tree species using hyperspectral imagery

Wang,

Jiang

2024

PLoS ONE

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In recent years, the advancement of hyperspectral remote sensing technology has greatly enhanced the detailed mapping of tree species. Nevertheless, delving deep into the significance of hyperspectral remote sensing data features for tree species recognition remains a challenging endeavor. The method of Hybrid-CS was proposed to addresses this challenge by synergizing the strengths of both deep learning and traditional learning techniques. Initially, we extract comprehensive correlation structures and spectral features. Subsequently, a hybrid approach, combining correlation-based feature selection with an optimized recursive feature elimination algorithm, identifies the most valuable feature set. We leverage the Support Vector Machine algorithm to evaluate feature importance and perform classification. Through rigorous experimentation, we evaluate the robustness of hyperspectral image-derived features and compare our method with other state-of-the-art classification methods. The results demonstrate: (1) Superior classification accuracy compared to traditional machine learning methods (e.g., SVM, RF) and advanced deep learning approaches on the tree species dataset. (2) Enhanced classification accuracy achieved by incorporating SVM and CNN information, particularly with the integration of attention mechanisms into the network architecture. Additionally, the classification performance of a two-branch network surpasses that of a single-branch network. (3) Consistent high accuracy across different proportions of training samples, indicating the stability and robustness of the method. This study underscores the potential of hyperspectral images and our proposed methodology for achieving precise tree species classification, thus holding significant promise for applications in forest resource management and monitoring.

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Section: Introductionmentioning

confidence: 99%

A Hybrid convolution neural network for the classification of tree species using hyperspectral imagery

Wang,

Jiang

2024

PLoS ONE

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Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network

Liu

2022

Computational Intelligence and Neuroscience

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Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were used to optimize the clustering dictionary, and then the image segmentation containing target information is completed. Finally, the image was input into the deep convolution neural network with a dual classifier, and the network detects the abnormal target in the image. The test results of this algorithm show that the structural similarity of the denoised image is higher than 0.86, which shows that this method has good noise reduction performance, image details will not damage, segmentation effect is good, and it can obtain high-definition target image information and accurately detect abnormal targets in the image.

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Multi-Classification and Recognition of Hyperspectral Remote Sensing Objects Based on Convolutional Neural Network

Cited by 2 publications

References 0 publications

A Hybrid convolution neural network for the classification of tree species using hyperspectral imagery

A Hybrid convolution neural network for the classification of tree species using hyperspectral imagery

Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network

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