Enhanced shuffle attention network based on visual working mechanism for high-resolution remote sensing image classification
| Autor: | Ming Cong, Jianjun Cui, Siliang Chen, Yihui Wang, Ling Han, Jiangbo Xi, Junkai Gu, Qingfang Zhang, Yiting Tao, Zhiye Wang, Miaozhong Xu, Hong Deng |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Geocarto International, Vol 37, Iss 27, Pp 18731-18766 (2022) |
| Druh dokumentu: | article |
| ISSN: | 10106049 1752-0762 1010-6049 |
| DOI: | 10.1080/10106049.2022.2143912 |
| Popis: | AbstractHigh-resolution remote sensing images provide complete and detailed ground scenes. However, these complex and diverse details make it difficult to interpret large area objects and their small area details at a single granularity. Therefore, the method proposed in this paper refers to the principles of visual working memory, visual attention and visual reasoning, independently analyse the ground objects of different granularities, and integrates the recognition results of the different granularity ground objects to form accurate classification results. This multi-granularity classification strategy suppresses noise interference, protects typical edges, and highlights clear details. Experimental results with dataset of real land use survey projects, including images of different time, multiple perspectives, and different altitudes, The results show that the idea of combining unsupervised edge detection with neural network classification can greatly enhance the generalization ability and edge cognitive ability, making the classification accuracy reach 80% on average, with an improvement of about 5%. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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