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.
Anand Karuppannan1, K. Subba Reddy2, Nilesh Madhukar Patil3, Chandra Mouli Venkata Srinivas Akana4
Gnanamani College of Technology, India1, Prakasam Engineering College, India2, SVKM Dwarkadas J Sanghvi College of Engineering, India3, Bonam Venkata Chalamayya Engineering College, India4
Spectral-Spatial, Deep DenseNet, Multispectral Image, Classification
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 14 , Issue: 1 , Pages: 3073 - 3078 )
Date of Publication :
August 2023
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394
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