| Autor: |
Quanlong Feng, Bowen Niu, Boan Chen, Yan Ren, Dehai Zhu, Jianyu Yang, Jiantao Liu, Cong Ou, Baoguo Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
International Journal of Applied Earth Observations and Geoinformation, Vol 102, Iss , Pp 102441- (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1569-8432 |
| DOI: |
10.1016/j.jag.2021.102441 |
| Popis: |
As the important components of modern facility agriculture, both plastic greenhouses and mulching films have been widely utilized in agriculture production. Due to the similarity of spectral signatures, it remains a challenging task to separate plastic greenhouses and mulching films from each other. Meanwhile, deep learning has achieved great performance in many computer vison tasks, and has become a research hotspot in remote sensing image analysis. However, deep learning has been rarely studied for the accurate mapping of agricultural plastic covers, especially for the long-neglected issue of the separation between plastic greenhouses and mulching films. Therefore, this study aims to propose a deep learning model to detect and separate plastic greenhouses and mulching films from very high resolution (VHR) remotely sensed data, providing the agricultural plastic covered maps for relevant decision-makers. In specific, the proposed model is a dilated and non-local convolutional neural network (DNCNN), which consists of several multi-scale dilated convolution blocks and a non-local feature extraction module. The former contains a series of dilated convolutions with various dilated rates, which is to aggregate multi-level spatial features hence to account for the scale variations of land objects. While the latter utilizes a non-local module to extract the global and contextual features to further enhance the inter-class separability. Experimental results from Shenxian, China and Al-Kharj, Saudi Arabia show that the DNCNN in this study obtains a high accuracy with an overall accuracy of 89.6% and 92.6%, respectively. Compared to standard convolution, the inclusion of dilated convolution could raise the classification accuracy by 2.7%. In addition, ablation analysis shows that the non-local feature extraction module could also improve the classification accuracy by about 2%. This study demonstrates that the proposed DNCNN yields an effective approach for the accurate agricultural plastic cover mapping from VHR remotely sensed imagery. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
Full Text from ScienceDirect