Editorial: Foreword to the Special Issue on Artificial Intelligence for Hyper- and Multispectral Remote Sensing Image Processing

被引：0
作者：
Bansal, Priti [1 ]
Palade, Vasile [2 ]
Piuri, Vincenzo [3 ]
机构：
[1] Netaji Subhas Univ Technol, New Delhi 110078, India
[2] Coventry Univ, Artificial Intelligence & Data Sci, Ctr Data Sci, Coventry CV1 5FB, England
[3] Univ Milan, Comp Engn, I-20133 Milan, Italy
来源：
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING | 2024年 / 17卷
关键词：
Special issues and sections; Multispectral imaging; Remote sensing; Artificial intelligence; Image processing;
D O I：
10.1109/JSTARS.2024.3379013
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In the current age of widespread application of artificial intelligence (AI) across various facets of life, satellite remote sensing is no outlier. Thanks to the ongoing enhancements in the spatial and temporal resolutions of satellite images, they are emerging as invaluable assets in areas such as land-use analysis, meteorology, change detection, and beyond. Accurate analysis and classification at various levels of hyperspectral images (HSIs) and multispectral remote sensing images (RSIs) are essential for extracting valuable insights from these datasets.
引用
收藏
页码：7212 / 7215
页数：4
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共 11 条
  • [1] Pan H., Gao F., Dong J., Du Q., Multiscale adaptive fusion network for hyperspectral image denoising, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 16, pp. 3045-3059, (2023)
  • [2] Chen X., Zhang X., Ren M., Zhou B., Feng Z., Cheng J., An improved hyperspectral unmixing approach based on a spatial–spectral adaptive nonlinear unmixing network, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 16, pp. 9680-9696, (2023)
  • [3] Jin D., Yang B., Graph attention convolutional autoencoder-based unsupervised nonlinear unmixing for hyperspectral images, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 16, pp. 7896-7906, (2023)
  • [4] Qu K., Li Z., A fast sparse NMF optimization algorithm for hyperspectral unmixing, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 17, pp. 1885-1902, (2024)
  • [5] Fang Y., Cai Y., Fan L., SDRCNN: A single-scale dense residual connected convolutional neural network for pansharpening, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 16, pp. 6325-6338, (2023)
  • [6] Pang S., Shi Y., Hu H., Ye L., Chen J., PTRSegNet: A patch-to-region bottom–up pyramid framework for the semantic segmentation of large-format remote sensing images, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 17, pp. 3664-3673, (2024)
  • [7] Hamza A., Et al., An integrated parallel inner deep learning models information fusion with bayesian optimization for land scene classification in satellite images, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 16, pp. 9888-9903, (2023)
  • [8] Singh A.K., Sunkara R., Kadambi G.R., Palade V., Spectral–spatial classification with naive bayes and adaptive FFT for improved classification accuracy of hyperspectral images, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 17, pp. 1100-1113, (2024)
  • [9] Chhapariya K., Buddhiraju K.M., Kumar A., A deep spectral–spatial residual attention network for hyperspectral image classification, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.
  • [10] Hong Z., Et al., Near real-time monitoring of fire spots using a novel SBT-FireNet based on Himawari-8 satellite images, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 17, pp. 1719-1733, (2024)
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