GIPERSPEKTRAL TASVIRLARNI TASNIFLASHDA FAZOVIY SIZIB CHIQISH NIMANI OʻLCHAYDI? YAQINLIK, KOʻRILMAGAN SINFLAR VA SINFLAR TARKIBIGA AJRATISH
DOI:
https://doi.org/10.5281/zenodo.23358282Abstract
Piksellar oʻqitish va test toʻplamlariga tasodifiy taqsimlanganda, giperspektral tasniflagichlarning aniqligi amaldagidan yuqori baholanadi. Ushbu tadqiqotda mazkur oshish qabul qiluvchi maydonga ega boʻlmagan ikkita tasniflagich yordamida uchta sahnada oʻlchandi va uchta tarkibiy qismga ajratildi: fazoviy yaqinlik, oʻqitish jarayonida birorta ham pikseli ishtirok etmagan sinflar hamda test toʻplamining tarkibi. Blokli va buferli ajratish usullari bilan taqqoslaganda, aniqlikdagi farq 10,7–47,4 foiz punktni tashkil etdi. Pavia University sahnasida ushbu farqning 70–78 foizi fazoviy yaqinlik bilan emas, balki oʻqitishda ishtirok etmagan sinflar bilan izohlanadi. Blok oʻlchami 32 piksel boʻlganda, Salinas sahnasining bitta ajratilishida 10 ta sinf oʻqitish jarayonidan butunlay chetda qoladi.Keywords
giperspektral tasvir, tasniflash, fazoviy avtokorrelyatsiya, maʼlumotlarning sizib chiqishi, baholash protokoli, blokli ajratish, eng yaqin qoʻshniReferences
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., Benediktsson, J. A. Deep learning for hyperspectral image classification: An overview. IEEE Transactions on Geoscience and Remote Sensing 57(9) (2019) 6690–6709.
Audebert, N., Le Saux, B., Lefèvre, S. Deep learning for classification of hyperspectral data: A comparative review. IEEE Geoscience and Remote Sensing Magazine 7(2) (2019) 159–173.
Muhamediyeva, D. T., Tukhtamuradov, N. U. Algorithm for recognition of images of agricultural crops. BIO Web of Conferences 71 (2023) 01007.
Muhamediyeva, D., Tukhtamurodov, N. Diagnostics system for plant leaf diseases using photo images. E3S Web of Conferences 401 (2023) 04023.
Belward, A. S., Lambin, E. Limitations to the identification of spatial structures from AVHRR data. International Journal of Remote Sensing 11(5) (1990) 921–927.
Friedl, M. A., Woodcock, C., Gopal, S., Muchoney, D., Strahler, A. H., Barker-Schaaf, C. A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data. International Journal of Remote Sensing 21(5) (2000) 1073–1077.
Liang, J., Zhou, J., Qian, Y., Wen, L., Bai, X., Gao, Y. On the sampling strategy for evaluation of spectral-spatial methods in hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 55(2) (2017) 862–880.
Hänsch, R., Ley, A., Hellwich, O. Correct and still wrong: The relationship between sampling strategies and the estimation of the generalization error. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017, pp. 3672–3675.
Feng, H., Wang, Y., Li, Z., Zhang, N., Zhang, Y., Gao, Y. Information leakage in deep learning-based hyperspectral image classification: A survey. Remote Sensing 15(15) (2023) 3793.
Kaufman, S., Rosset, S., Perlich, C., Stitelman, O. Leakage in data mining: Formulation, detection, and avoidance. ACM Transactions on Knowledge Discovery from Data 6(4) (2012) 15.
Kapoor, S., Narayanan, A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4(9) (2023) 100804.
Roberts, D. R., Bahn, V., Ciuti, S., et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40(8) (2017) 913–929.
Ploton, P., Mortier, F., Réjou-Méchain, M., et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nature Communications 11 (2020) 4540.
Nalepa, J., Myller, M., Kawulok, M. Validating hyperspectral image segmentation. IEEE Geoscience and Remote Sensing Letters 16(8) (2019) 1264–1268.
Valavi, R., Elith, J., Lahoz-Monfort, J. J., Guillera-Arroita, G. blockCV: An R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution 10(2) (2019) 225–232.
Graña, M., Veganzons, M. A., Ayerdi, B. Hyperspectral remote sensing scenes. Computational Intelligence Group, University of the Basque Country. URL: https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_ Scenes
Cover, T., Hart, P. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1) (1967) 21–27.
Congalton, R. G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37(1) (1991) 35–46.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 MUHANDISLIK VA IQTISODIYOT
This work is licensed under a Creative Commons Attribution 4.0 International License.
