Deep Learning-based Detection of Feature Changes in Arable Land
In economically developed regions, the occupation of arable land has long been a significant concern, and identifying cultivated land accurately is crucial for mitigating this issue. To address the problem of "pseudo-change" caused by complex backgrounds, uneven brightness, and natural growth, we propose a change detection algorithm based on twin neural networks. Our algorithm comprises two sub-networks and a decision layer that use a multi-scale feature model and an improved spatio-temporal attention module to extract and process features from two temporal images. We validate our algorithm using high-resolution UAV aerial photography data with a resolution of 0.3m and 0.05m. Our proposed method achieves an F1 coefficient of over 87% in the cultivated land feature change detection dataset, which is more than 45% higher than the traditional STANet. The contribution of our research lies in developing a more effective method for identifying cultivated land in economically developed regions, which can help mitigate the serious "man-made disaster" of arable land occupation. Our proposed method has the potential to contribute significantly to the field of precision agriculture and land resource management.
Copyright (c) 2023 Journal of Asian Geography
This work is licensed under a Creative Commons Attribution 4.0 International License.