A review of deep learning used in the hyperspectral image analysis for agriculture

Author:

Wang Chunying,Liu Baohua,Liu Lipeng,Zhu Yanjun,Hou Jialin,Liu PingORCID,Li Xiang

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference185 articles.

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2. Ahmad M, Shabbir S, Oliva D, Mazzara M, Distefano S (2020a) Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification. Optik 206:163712

3. Ahmad M, Mazzara M, Raza RA, Distefano S, Sohaib A (2020b) Multiclass non-randomized spectral–spatial active learning for hyperspectral image classification. Appl Sci 10(14):4739

4. Athanasios V, Nikolaos D, Anastasios D, Eftychios P (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci 2018:1–13

5. Awad MM (2019) An innovative intelligent system based on remote sensing and mathematical models for improving crop yield estimation. Inf Process Agric 6(3):316–325

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