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

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 v...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2019-09
Main Authors: Wu, Chunxue, Bobo Ju, Xiong, Naixue, Yang, Guisong, Wu, Yan, Yang, Hongming, Huang, Jiaying, Xu, Zhiyong
Format: Article
Language:English
Subjects:
Detection
Image segmentation
Land cover
Mathematical analysis
Measurement methods
Remote sensing
Satellite imagery
Semantic segmentation
Semantics
Soil water
Vegetation
Woodlands
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422