Hyperspectral Image Study based on Deep Learning Model HSI-ConvNext

Zhonghui Bian; Xuying Zhao; Qian Wang; Zhibo Xing

doi:10.62051/6bb78m23

Authors

Zhonghui Bian
Xuying Zhao
Qian Wang
Zhibo Xing

DOI:

https://doi.org/10.62051/6bb78m23

Keywords:

HSI-ConvNext; Hyperspectral Image Processing; Hyperspectral Imaging; Hyperspectral Data Analysis; Spectral Characterization.

Abstract

In recent years, deep learning techniques have made significant research progress in the field of hyperspectral image processing. Hyperspectral images are widely used in agriculture, environmental monitoring, geological exploration and other fields due to their richness in material and spectral information about objects. Deep learning models play an important role in hyperspectral image research and are able to learn feature representations from large-scale high-dimensional data, improving the performance of tasks such as classification, segmentation, and detection. In the study, researchers usually improve and optimize for the features of hyperspectral images by constructing deep learning models such as HSI-ConvNext. It is able to capture the correlation between different bands, thus realizing semantic segmentation and target detection for hyperspectral images. Data augmentation can effectively expand the limited hyperspectral dataset, and migration learning is able to migrate the knowledge of models trained in other domains to hyperspectral image tasks. Realistic hyperspectral images can be generated for data enhancement and model training. In conclusion, the study of hyperspectral images based on the deep learning model HSI-ConvNext provides new methods and techniques for the analysis and application of hyperspectral data. In the future, with the continuous development of deep learning technology, we can expect more innovations and breakthroughs in the field of hyperspectral image processing.

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References

Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physical D: Nonlinear Phenomena, 404, 132306.

Jiao, M., Wang, D., & Qiu, J. (2020). A GRU-RNN based momentum optimized algorithm for SOC estimation. Journal of Power Sources, 459, 228051.

Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS journal of photogrammetry and remote sensing, 173, 24-49.

Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., ... & Ghayvat, H. (2021). CNN variants for computer vision: History, architecture, application, challenges and future scope. Electronics, 10(20), 2470.

Wen, L., Li, X., & Gao, L. (2020). A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Computing and Applications, 32, 6111-6124.

He, F., Liu, T., & Tao, D. (2020). Why resnet works? residuals generalize. IEEE transactions on neural networks and learning systems, 31(12), 5349-5362.

Wu, Z., Shen, C., & Van Den Hengel, A. (2019). Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recognition, 90, 119-133.

Zhou, T., Zhao, Y., & Wu, J. (2021, January). Resnext and res2net structures for speaker verification. In 2021 IEEE Spoken Language Technology Workshop (SLT) (pp. 301-307). IEEE.

Pant, G., Yadav, D. P., & Gaur, A. (2020). ResNeXt convolution neural network topology-based deep learning model for identification and classification of Pediastrum. Algal research, 48, 101932.

Zhu, Y., Yuan, K., Zhong, W., & Xu, L. (2023). Spatial-Spectral ConvNeXt for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Liu, D., Lv, F., Wang, C., Wang, G., Zhang, H., & Guo, J. (2023). Classification of early mechanical damage over time in pears based on hyperspectral imaging and transfer learning. Journal of Food Science.

Zhang, X., Yan, J., Tian, J., Li, W., Gu, X., & Tian, Q. (2023). Objective evaluation-based efficient learning framework for hyperspectral image classification. GIScience & Remote Sensing, 60(1), 2225273.

Wu, H., Dai, S., Liu, C., Wang, A., & Iwahori, Y. (2023). A Novel Dual-Encoder Model for Hyperspectral and LiDAR Joint Classification via Contrastive Learning. Remote Sensing, 15(4), 924.

Miao, J., Huang, Y., Wang, Z., Wu, Z., & Lv, J. (2023). Image recognition of traditional Chinese medicine based on deep learning. Frontiers in Bioengineering and Biotechnology, 11.

Liu, Y., Yao, W., Qin, F., Zhou, L., & Zheng, Y. (2023). Spectral Classification of Large-Scale Blended (Micro) Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning. Environmental Science & Technology, 57(16), 6656-6663.

Feng, F., Zhang, Y., Zhang, J., & Liu, B. (2023). Low-Rank Constrained Attention-Enhanced Multiple Spatial–Spectral Feature Fusion for Small Sample Hyperspectral Image Classification. Remote Sensing, 15(2), 304.

Hassanien, M. A., Singh, V. K., Puig, D., & Abdel-Nasser, M. (2022). Predicting breast tumor malignancy using deep ConvNeXt radiomics and quality-based score pooling in ultrasound sequences. Diagnostics, 12(5), 1053.