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Karuppannan, Anand
- Spectral-Spatial Deep Densenet Learning for Multispectral Image Classification and Analysis
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Authors
Anand Karuppannan
1,
K. Subba Reddy
2,
Nilesh Madhukar Patil
3,
Chandra Mouli Venkata Srinivas Akana
4
Affiliations
1 Department of Electronics and Communication Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science and Engineering, Prakasam Engineering College, IN
3 Department of Computer Engineering, SVKM Dwarkadas J Sanghvi College of Engineering, IN
4 Bonam Venkata Chalamayya Engineering College, IN
1 Department of Electronics and Communication Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science and Engineering, Prakasam Engineering College, IN
3 Department of Computer Engineering, SVKM Dwarkadas J Sanghvi College of Engineering, IN
4 Bonam Venkata Chalamayya Engineering College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 1 (2023), Pagination: 3073-3078Abstract
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, ClassificationReferences
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