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Spectral-Spatial Deep Densenet Learning for Multispectral Image Classification and Analysis


Affiliations
1 Department of Electronics and Communication Engineering, Gnanamani College of Technology, India
2 Department of Computer Science and Engineering, Prakasam Engineering College, India
3 Department of Computer Engineering, SVKM Dwarkadas J Sanghvi College of Engineering, India
4 Bonam Venkata Chalamayya Engineering College, India
     

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In this research, a novel model for multispectral image classification and analysis, leveraging Spectral-Spatial Deep DenseNet Learning is presented. This proposed framework combines spectral and spatial information to enhance the discriminative power of deep neural networks, enabling accurate classification of multispectral images. We conduct extensive experiments on benchmark datasets, demonstrating the superior performance of our method compared to existing approaches. Furthermore, we provide a comprehensive analysis of the learned features, shedding light on the interpretability and effectiveness of our model for multispectral image analysis tasks.

Keywords

Spectral-Spatial, Deep DenseNet, Multispectral Image, Classification
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  • Spectral-Spatial Deep Densenet Learning for Multispectral Image Classification and Analysis

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Authors

Anand Karuppannan
Department of Electronics and Communication Engineering, Gnanamani College of Technology, India
K. Subba Reddy
Department of Computer Science and Engineering, Prakasam Engineering College, India
Nilesh Madhukar Patil
Department of Computer Engineering, SVKM Dwarkadas J Sanghvi College of Engineering, India
Chandra Mouli Venkata Srinivas Akana
Bonam Venkata Chalamayya Engineering College, India

Abstract


In this research, a novel model for multispectral image classification and analysis, leveraging Spectral-Spatial Deep DenseNet Learning is presented. This proposed framework combines spectral and spatial information to enhance the discriminative power of deep neural networks, enabling accurate classification of multispectral images. We conduct extensive experiments on benchmark datasets, demonstrating the superior performance of our method compared to existing approaches. Furthermore, we provide a comprehensive analysis of the learned features, shedding light on the interpretability and effectiveness of our model for multispectral image analysis tasks.

Keywords


Spectral-Spatial, Deep DenseNet, Multispectral Image, Classification

References