DEVELOPMENT OF AEROSPACE IMAGES PRELIMINARY PROCESSING METHOD FOR SUBSEQUENT RECOGNITION AND IDENTIFICATION OF VARIOUS OBJECTS
DOI:
https://doi.org/10.37943/18BIAC9844Keywords:
data compression, hyperspectral images, interband correlation, difference transformations, lossless, compression algorithmAbstract
Nowadays, the application of hyperspectral images is vital for every section of the humanity life such as agrotechnical research for the field condition state and water security. This article presents a new lossless data compression algorithm focused on the processing of hyperspectral aerospace images. The algorithm takes into account inter-band correlation and difference transformations to effectively reduce the range of initial values. correlation allows you to find the best reference channel that defines the sequence of operations in the algorithm, which contributes to a significant increase in the compression ratio while maintaining high data quality. The practical implementation of the algorithm lies in the process of the transfer the lower size file with high efficiency for unmanned aerial vehicle and satellites to save more computational resources. This method demonstrates high computational efficiency and can be applied to various tasks that require efficient storage and transmission of hyperspectral images. The importance of processing hyperspectral data and the problems associated with their volume and complexity of analysis were described. Current approaches to data compression are considered and their limitations are identified, which justifies the need to develop new methods. The relevance and necessity of effective compression algorithms for aerospace applications is emphasized. An analysis of existing methods and algorithms for compressing hyperspectral data was carried out. Particular attention is paid to approaches that use cross-channel correlation and difference transformations. The effectiveness of current methods is evaluated and their shortcomings are identified, which serves as a justification for the development of a new algorithm. A developed lossless data compression algorithm based on the use of inter-band correlation and difference transformations was described. The stages of forming groups of channels and the selection of optimal compression parameters are considered in detail. The method of determining the reference channel, which sets the sequence of operations in the algorithm, which provides more efficient data compression, is especially noted. The advantages and possible limitations of the new approach, as well as its potential for practical use, are discussed. It was noted that the developed method successfully solves the problems associated with the volume of hyperspectral data, providing a high compression ratio without quality loss. The prospects for further development of the algorithm and its application in various fields of science and technology are discussed.
References
Su, P., Liu, D., Li, X., & Liu, Z. (2018). A saliency-based band selection approach for hyperspectral imagery inspired by scale selection. IEEE Geoscience and Remote Sensing Letters, 15(4), 572-576. https://doi.org/10.1109/LGRS.2018.2800034
Dou, Z., Gao, K., Zhang, X., Wang, H., & Han, L. (2021). Band selection of hyperspectral images using attention-based autoencoders. IEEE Geoscience and Remote Sensing Letters, 18(1), 147-151. https://doi.org/10.1109/LGRS.2020.2967815
Feng, Y., Yuan, Y., & Lu, X. (2016). A non-negative low-rank representation for hyperspectral band selection. International Journal of Remote Sensing, 37(19), 4590-4609. https://doi.org/10.1080/01431161.2016.1214299
Zhong, S., et al. (2019). Class feature weighted hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(12), 4728-4745. https://doi.org/10.1109/JSTARS.2019.2950876
Wang, C., Zhang, L., Wei, W., & Zhang, Y. (2020). Hyperspectral image classification with data augmentation and classifier fusion. IEEE Geoscience and Remote Sensing Letters, 17(8), 1420-1424. https://doi.org/10.1109/LGRS.2019.2945848
Yu, C., Han, R., Song, M., Liu, C., & Chang, C.-I. (2022). Feedback attention-based dense CNN for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16, Article 5501916. https://doi.org/10.1109/TGRS.2021.3058549
Paoletti, M. E., Haut, J. M., Fernandez-Beltran, R., Plaza, J., Plaza, A., Li, J., & Pla, F. (2018). Capsule networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 1, 1-16. https://doi.org/10.1109/tgrs.2018.2871782
Haut, J. M., Bernabé, S., Paoletti, M. E., Fernandez-Beltran, R., Plaza, A., & Plaza, J. (2019). Low-high-power consumption architectures for deep-learning models applied to hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 16(5), 776-780. https://doi.org/10.1109/LGRS.2018.2881045
Wei, W., Li, W., Zhang, L., Wang, C., Zhang, P., & Zhang, Y. (2019). Robust hyperspectral image domain adaptation with noisy labels. IEEE Geoscience and Remote Sensing Letters, 16(7), 1135-1139. https://doi.org/10.1109/LGRS.2018.2889800
He, N., et al. (2019). Feature extraction with multiscale covariance maps for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(2), 755-769. https://doi.org/10.1109/TGRS.2018.2860464
Erturk, A. (2015). Enhanced unmixing-based hyperspectral image denoising using spatial preprocessing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 2720-2727. https://doi.org/10.1109/jstars.2015.2439031
Delgado, J., Martín, G., Plaza, J., Jiménez, L. I., & Plaza, A. (2016). Fast spatial preprocessing for spectral unmixing of hyperspectral data on graphics processing units. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(2), 952-961. https://doi.org/10.1109/JSTARS.2015.2495128
Kowkabi, F., & Keshavarz, A. (2019). Using spectral geodesic and spatial Euclidean weights of neighbourhood pixels for hyperspectral endmember extraction preprocessing. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 201-218. https://doi.org/10.1016/j.isprsjprs.2019.1
Sarinova, A., Dunayev, P., Bekbayeva, A., Mekhtiyev, A., & Sarsikeyev, Y. (2022). Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations. Eastern-European Journal of Enterprise Technologies, 1(2), 22-30. https://doi.org/10.15587/1729-4061.2022.251404
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