In this paper, we propose a novel approach for atmospheric correction of hyperspectral images based on machine learning.
This paper proposes a novel approach for atmospheric correction of hyperspectral images based on machine learning that outperforms QUAC, and the retrieved ...
May 11, 2020 · Sijie Zhu, Bin Lei, Yirong Wu: Retrieval of Hyperspectral Surface Reflectance Based on Machine Learning. Remote. Sens. 10(2): 323 (2018).
Retrieval of Hyperspectral Surface Reflectance Based on Machine Learning · School of Electronic, Electrical and Communication Engineering, University of the ...
Our study reveals that hyperspectral and physiological changes in response to RKN and DS could help diagnose plant health before visual symptoms.
Nov 3, 2016 · Results show that retrieval of AOT from the remote sensing data required establishing empirical relationships between 465.6nm, 659nm and 2105nm ...
Missing: Machine Learning.
In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest.
Many methods based on radiative-transfer models and empirical approaches with prior knowledge have been developed for the retrieval of hyperspectral surface ...
This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food ...
Missing: Retrieval | Show results with:Retrieval
Feb 27, 2019 · We present a physical-based atmospheric correction algorithm for land surface reflectance retrieval based on radiative transfer model MODTRAN 5, ...
Missing: Machine | Show results with:Machine