التفاصيل البيبلوغرافية
| العنوان: |
Recognition of cotton distribution based on GF - 2 images and Unet model. (English) |
| المؤلفون: |
Anwar, ERPAN, Sawut, MAMAT, Balat, MAIHEMUTI |
| المصدر: |
Remote Sensing for Natural Resources; Jun2022, Vol. 34 Issue 2, p242-250, 9p |
| مصطلحات موضوعية: |
COTTON, DEEP learning, MACHINE learning, REMOTE sensing |
| مستخلص: |
The typical crop cotton in the Ugan - Kuqa River Delta Oasis was used as the research object to study the applicability and optimization process of the deep learning method in the identification of coUon distribution in arid areas. Based on the domestic GF -2 images and the field survey data, the Unet deep learning method was adopted, in which the characteristics of the Unet network ' s multifile convolution operations were fully utilized to explore the deep - level characteristics of coUon in remote sensing images, thereby improving the precision of coUon extraction. The results show that the recognition rffect of the Unet model to extract coUon, corn, and peppers in the study area is better than the classification results of the object - oriented method and the traditional machine learning algorithms. The overall precision is 84. 22%, and the Kappa coefficient is 0. 804 7. Compared with the object - oriented method and the traditional machine learning algorithms SVM and RF, the overaH precision has increased by 7.94 percentage points, 11. 93 percentage points, and 11.73 percentage points, respectively, and the Kappa coeeficient has increased by 10. 13%, 14.72%, and 14.60%, respectively. In the classification results of the Unet model, both the mapping precision and the user precision of cotton are higher than those of the other three methodss which are 94. 95% and 89. 07%, respectively. Therefore, it is feasible and reliable to use the Unet model to extract high - precision cotton spatial distribution information of arid areas on GF - 2 high - resolution remote sensing images. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
