U-net super-neural segmentation and similarity calculation to realize vegetation change assessment in satellite imagery
This addresses the problem of monitoring vegetation changes for environmental research, but it is incremental as it applies existing methods to a specific domain.
The paper tackles automated vegetation change assessment in satellite imagery by using U-net for semantic segmentation and an integral progressive method to calculate forestland change rates, achieving automated valuation of woodland change rates.
Vegetation is the natural linkage connecting soil, atmosphere and water. It can represent the change of land cover to a certain extent and serve as an indicator for global change research. Methods for measuring coverage can be divided into two types: surface measurement and remote sensing. Because vegetation cover has significant spatial and temporal differentiation characteristics, remote sensing has become an important technical means to estimate vegetation coverage. This paper firstly uses U-net to perform remote sensing image semantic segmentation training, then uses the result of semantic segmentation, and then uses the integral progressive method to calculate the forestland change rate, and finally realizes automated valuation of woodland change rate.