A Nonlinear Radiometric Normalization Model for Satellite Imgaes Time Series Based on Artificial Neural Networks and Greedy Algroithm

A Nonlinear Radiometric Normalization Model for Satellite Imgaes Time Series Based on Artificial Neural Networks and Greedy Algroithm

Satellite Image Time Series (SITS) is a data set that includes satellite images across several years with a high acquisition rate. Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using SITS due to the radiometric distortion caused by nois...

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Bibliographic Details
Main Authors: Zhaohui Yin, Lejun Zou, Jiayu Sun, Haoran Zhang, Wenyi Zhang, Xiaohua Shen
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Remote Sensing
Subjects:
satellite image time series
radiometric normalization
nonlinear radiometric distortion
artificial neural networks
Online Access:https://www.mdpi.com/2072-4292/13/5/933

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