U-net super-neural segmentation and similarity calculation to realize vegetation change assessment in satellite imagery

TL;DR

This paper introduces a U-net based method for semantic segmentation of satellite imagery to automatically assess vegetation change rates, combining deep learning with a novel similarity calculation approach.

Contribution

It proposes a new approach integrating U-net segmentation with an integral progressive method for automated woodland change rate estimation.

Findings

Effective vegetation change detection in satellite images

Automated woodland change rate calculation achieved

Enhanced remote sensing analysis accuracy

Abstract

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.