Refine your search
Collections
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Deepa, A.J.
- Deep Generative Discrete Cosine Transform for Spectral Image Processing
Abstract Views :112 |
PDF Views:1
Authors
Affiliations
1 Department of Mathematics, St. Xavier’s Catholic College of Engineering, IN
2 Department of Computer Science and Engineering, ILAHIA College of Engineering and Technology, IN
3 Department of Computer Science and Engineering, Karunya Institute of Technology and Science, IN
4 Department of Computer Science and Engineering, Ponjsely College of Engineering, IN
5 Department of Computer Science and Engineering, IES College of Engineering, IN
1 Department of Mathematics, St. Xavier’s Catholic College of Engineering, IN
2 Department of Computer Science and Engineering, ILAHIA College of Engineering and Technology, IN
3 Department of Computer Science and Engineering, Karunya Institute of Technology and Science, IN
4 Department of Computer Science and Engineering, Ponjsely College of Engineering, IN
5 Department of Computer Science and Engineering, IES College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 4 (2022), Pagination: 2746-2749Abstract
The ever-increasing number of publications and applications in the field of cross-spectral image processing has led to the area receiving greater focus than it previously had. In cross-spectral frameworks, the data from hyperspectral bands is blended with the data from other spectral bands in order to provide responses that are more robust to particular obstacles. Cross-spectral processing could be useful for a variety of applications, including dehazing, segmentation, calculating the vegetation index, and face identification, to name just a few of them. The availability of cross-and multi-spectral camera sets on the market, such as smartphones, multispectral cameras for remote sensing, or multi-spectral cameras for automotive systems or drones, has spawned an increased number of applications for these cameras. In this paper, we develop a deep generative discrete cosine transform for possible image processing for the enhancing the quality of images. This is conducted to improve the prediction or classification ability by the classifiers on hyperspectral images. The models are validated with various machine learning classifiers. The results of simulation shows that the proposed method higher degree of accuracy than the existing methods.Keywords
Deep Generative Model, Discrete Cosine Transform, Spectral Image Processing
References
- Naoto Yokoya, Claas Grohnfeldt and Jocelyn Chanussot. “Hyperspectral and Multispectral Data Fusion: A Comparative Review of the Recent Literature”, IEEE Geoscience and Remote Sensing Magazine, Vol. 5, No. 2, pp. 29-56, 2017.
- Renwei Dian, Shutao Li, Leyuan Fang, Ting Lu and Jose M. Bioucas-Dias, “Nonlocal Sparse Tensor Factorization for Semiblind Hyperspectral and Multispectral Image Fusion”, IEEE Transactions on Cybernetics, Vol. 50, No. 10, pp. 4469-4480, 2019.
- Xuelong Li, Yue Yuan and Qi Wang, “Hyperspectral and Multispectral Image Fusion via Nonlocal Low-Rank Tensor Approximation and Sparse Representation”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, No. 1, pp. 550-562, 2020.
- Mishra, P., & Herrmann, I. (2021). GAN meets chemometrics: Segmenting spectral images with pixel2pixel image translation with conditional generative adversarial networks. Chemometrics and Intelligent Laboratory Systems, 215, 104362.
- Fei Ma, Feixia Yang, Ziliang Ping and Wenqin Wang, “Joint Spatial-Spectral Smoothing in a Minimum-Volume Simplex for Hyperspectral Image Super-Resolution”, Applied Sciences, Vol. 10, No. 1, pp. 237-256, 2019.
- D. Hong, N. Yokoya, J. Chanussot and X. Zhu, “An Augmented Linear Mixing Model to Address Spectral Varialbilty for Hyperspectral Unmixing, Geography”, IEEE Transactions on Image Processing, Vol. 54, No. 3, pp. 1-17, 2018.
- Naoto Yokoya, Takehisa Yairi and Akira Iwasaki, “Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 2, pp. 528-537, 2012.
- Li Sun, Kang Zhao and Ziwen Liu, “Enhancing Hyperspectral Unmixing With Two-Stage Multiplicative Update Nonnegative Matrix Factorization”, IEEE Access, Vol. 7, pp. 171023-171031, 2019.
- R. Dhaya, “Hybrid Machine Learning Approach to Detect the Changes in SAR Images for Salvation of Spectral Constriction Problem”, Journal of Innovative Image Processing, Vol. 3, No. 2, pp. 118-130, 2021.
- C. Yu, Y. Liu and Z. Hu, Z. (2022). Multi-branch Feature Difference Learning Network for Cross-Spectral Image Patch Matching. IEEE Transactions on Geoscience and Remote Sensing, Vol. 87, pp. 1-14, 2022.