ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

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
     

   Subscribe/Renew Journal


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
Subscription Login to verify subscription
User
Notifications
Font Size

  • Anil. K. Jain, “Fundamental of Digital Image Processing”, PHI Publication, 2014.

  • Dominic Rufenacht, Reji Mathew and David Taubman, “Novel Motion Field Anchoring Paradigm for Highly Scalable Wavelet-Based Video Coding”, IEEE Transactions on Image Processing, Vol. 25, No. 1, pp. 39-52, 2016.

  • S. Mallat, “A Wavelet Tour of Signal Processing”, Academic Press, 2008.

  • Weiqi Luo, Fangjun Huang and Jiwu Huang, “A more Secure Steganography based on Adaptive Pixel-value Differencing Scheme”, Multimedia Tools and Applications, Vol. 52, No. 2-3, pp. 407-430, 2011.

  • M.S. Sutaone and M.V. Khandare, “Image based Steganography using LSB Insertion Technique”, Proceedings of IET International Conference on Wireless, Mobile and Multimedia Networks, pp. 146-151, 2008.

  • K.N.G. Veerappan, J. Perumal and S.J.N. Kumar, “Categorical Data Clustering using Meta Heuristic LinkBased Ensemble Method: Data Clustering using Soft Computing Techniques”, Proceedings of IEEE International Conference on Dynamics of Swarm Intelligence Health Analysis for the Next Generation, pp. 226-238, 2023.

  • J.C. Galan-Hernandez, V. Alarcon-Aquino, O. Starostenko, J.M. Ramirez-Cortes and Pilar Gomez-Gil, “Wavelet-based Frame Video Coding Algorithms using FOVEA and SPECK”, Engineering Applications of Artificial Intelligence, Vol. 69, pp. 127-136, 2018.

  • Vikrant Bhateja, Mukul Misra, Shabana Urooj and Aimé Lay-Ekuakille, “Bilateral Despeckling Filter in Homogeneity Domain for Breast Ultrasound Images”, Proceedings of International Conference on Advances in Computing, Communications and Informatics, pp. 1027- 1032, 2014.

  • D. Irfan, S. Srivastava and V. Saravanan, “Prediction of Quality Food Sale in Mart using the AI-Based TOR Method”, Journal of Food Quality, Vol. 2022, pp. 1-12, 2022.

  • G. Kiruthiga, “Improved Object Detection in Video Surveillance using Deep Convolutional Neural Network Learning”, International Journal for Modern Trends in Science and Technology, Vol. 7, No. 11, pp. 108-114, 2021.

  • Mohamed Saleh Abuazoum, “Efficient Analysis of Medical Image De-noising for MRI and Ultrasound Images”, Master Thesis, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein onn Malaysia, pp. 1-121, 2012.

  • K. Thangavel, R. Manavalan and I. Laurence Aroquiaraj, “Eliminating Speckle Noise from Ultrasound Medical Images: a Non-Linear Approach”, Narosa Publishing House, 2009.


Abstract Views: 50

PDF Views: 1




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

Abstract Views: 50  |  PDF Views: 1

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