Abstract
The problem of small sample classification is to identify image categories that have not appeared in the training concentration when marking the scarce sample samples of the training data set. Such tasks are of great significance in the recognition of remote sensing scenarios. It is a problem worth studying in this field. As we all know, training a deep learning model for classification requires a considerable labeling data set, which makes the production of training data sets huge. In this article, we propose a MADB feature extraction model based on Mixed Attention Module as a base model to extract features. Using RccaEMD module as the measurement algorithm to distinguish the classification of remote sensing scenarios. In NWPU-RESISC45 dataset, AID dataset, and UC-Merced dataset, it proves that our method has achieved higher accuracy than the current advanced methods of this field.
This work is supported by the National Key R&D Program of China under Grant 2022YFF0503900.
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Wang, K., Wang, Y., Ding, Z. (2024). MADB-RemdNet for Few-Shot Learning in Remote Sensing Classification. In: Meng, X., Zhang, X., Guo, D., Hu, D., Zheng, B., Zhang, C. (eds) Spatial Data and Intelligence. SpatialDI 2024. Lecture Notes in Computer Science, vol 14619. Springer, Singapore. https://doi.org/10.1007/978-981-97-2966-1_19
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