HSI Classification Based on Multimodal CNN and Shadow Enhance by DSR Spatial-Spectral Fusion

Li, Xuefeng; Wang, Hao; Liu, Jiaming; Sun, Shibo; Fu, Min

doi:10.1080/07038992.2021.1960810

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…Among them, the latter combines the target detection algorithm, attention mechanism, reinforcement learning and other methods, which not only saves the cost of manual annotation, but also has more research value for practical application and promotion on the basis of meeting or even exceeding the FGI classification method based on strongly supervised information [6] In addition, Chen X proposed an improved model fusing segmented linear representation and weighted support vector machine for the problems related to FGI classification by applying it in real stock image analysis, thus effectively improving the recognition accuracy of FGI [11]. Liu X et al addressed the problem of recognizing and classifying hyperspectral FGIs by organically integrating convolutional and multimodal neural network models on the basis of dynamic stochastic resonance, thus effectively improving the recognition accuracy of FGIs based on the use of MMFs [12]. Jenisha J et al proposed a fusion model for automated FGI segmentation by using deep ML and attention mechanism for problems related to FGI recognition segmentation of liver tumors, thus in providing help in enhancing the segmentation of liver tumor images [13].…”

Section: Related Workmentioning

confidence: 99%

Multi-Model Fusion Fine-Grained Image Classification Method Based on Migration Learning

Zhang,

Wang

2024

IEEE Access

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Current single-model methods for fine-grained image classification suffer from insufficient generalisation ability, while multi-model fusion methods suffer from weight curing. The study suggests and experimentally tests a dynamic weight multi-model fusion strategy for transfer learning-based fine-grained picture classification. The results of the experiment showed that the suggested fusion model enhanced recognition accuracy by 1.33%, 1.19%, and 0.83% compared to the single model on the medical dataset and 3.25%, 1.34%, and 7.28% on the agronomy dataset, respectively. Furthermore, when compared to the comparison method, the models under the proposed method of the study improved recognition accuracy by 0.18%, 0.61%, 0.43%, and 0.43% on the medical dataset, and the experimental time consumed was 3.25 minutes less than that of the sum-of-maximum-probabilities method; however, the fusion models of the proposed method of the study had higher recognition accuracy than that of the comparison met Overall, the proposed dynamic weight multi-model fusion method for fine-grained image classification using migration learning has better performance and generalisation ability, which can improve performance while reducing time cost, and has higher application value for the actual fine-grained image classification task.

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Section: Related Workmentioning

confidence: 99%

Multi-Model Fusion Fine-Grained Image Classification Method Based on Migration Learning

Zhang,

Wang

2024

IEEE Access

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Intelligent Rider Optimization Algorithm with Deep Learning Enabled Hyperspectral Remote Sensing Imaging Classification

Dutta

Alsanea²,

Qureshi

et al. 2022

Canadian Journal of Remote Sensing

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Shadow Enhancement Using 2D Dynamic Stochastic Resonance for Hyperspectral Image Classification

Liu

2023

Remote Sensing

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With the development of remote sensing technology, classification has become a meaningful way to explore the rich information in hyperspectral images (HSIs). However, various environmental factors may cause noise and shadow areas in HSIs, resulting in weak signals and difficulties in fully utilizing information. In addition, classification methods based on deep learning have made considerable progress, but features extracted from most networks have much redundancy. Therefore, a method based on two-dimensional dynamic stochastic resonance (2D DSR) shadow enhancement and convolutional neural network (CNN) classification combined with an attention mechanism (AM) for HSIs is proposed in this paper. Firstly, to protect the spatial correlation of HSIs, an iterative equation of 2D DSR based on the pixel neighborhood relationship was derived, which made it possible to perform matrix SR in the spatial dimension of the image, instead of one-dimensional vector resonance. Secondly, by using the noise in the shadow area to generate resonance, 2D DSR can help increase the signals in the shadow regions by preserving the spatial characteristics, and enhanced HSIs can be obtained. Then, a 3DCNN embedded with two efficient channel attention (ECA) modules and one convolutional block attention module (CBAM) was designed to make the most of critical features that significantly affect the classification accuracy by giving different weights. Finally, the performance of the proposed method was evaluated on a real-world HSI, and comparative studies were carried out. The experimental results showed that the proposed approach has promising prospects in HSIs’ shadow enhancement and information mining.

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HSI Classification Based on Multimodal CNN and Shadow Enhance by DSR Spatial-Spectral Fusion

Cited by 4 publications

References 48 publications

Multi-Model Fusion Fine-Grained Image Classification Method Based on Migration Learning

Multi-Model Fusion Fine-Grained Image Classification Method Based on Migration Learning

Intelligent Rider Optimization Algorithm with Deep Learning Enabled Hyperspectral Remote Sensing Imaging Classification

Shadow Enhancement Using 2D Dynamic Stochastic Resonance for Hyperspectral Image Classification

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