Affiliation:
1. Guangxi University of Science and Technology
Abstract
Abstract
Aiming at the feature extraction of farmland from remote sensing images relying on greatly manual interpretation, which consumes a lot of manpower, financial resources, and is inefficient. This paper proposes a remote sensing image farmland recognition method based on a transfer deep learning model. We also propose a simple but effective method to overcome the problems of unclear edge segmentation and partial field of view of the convolutional network. The experimental results show that the PA of the U-Net network model has reached PA 0.9124, mPA 0.7757, mIoU 0.6832, Recall 0.9586, Precision 0.94215, F1-Score 0.9503. We have obtained a competitive result and the U-Net neural network has an excellent capacity for remote sensing image farmland recognition, robustness, and practicability.
Publisher
Research Square Platform LLC