Chlorophyll Retrieval Using Ground Based Hyperspectral Data from a Tropical Area of India Using Regression Algorithms

2014 ◽  
pp. 177-194 ◽  
Author(s):  
M. Gupta ◽  
Prashant K. Srivastava ◽  
S. Mukherjee ◽  
G. Sandhya Kiran
Keyword(s):  
Hyperspectral Data ◽  
Tropical Area ◽  
Regression Algorithms

scholarly journals Direct or indirect on-shore hydrocarbon detection methods applied to hyperspectral data in tropical area

2018 ◽  
Author(s):  
Sophie Fabre ◽  
Alexandre Alakian ◽  
Dominique Dubucq ◽  
Philippe Déliot ◽  
Véronique Achard
Keyword(s):  
Hyperspectral Data ◽  
Detection Methods ◽  
Hydrocarbon Detection ◽  
Tropical Area

Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms

2019 ◽  
Vol 232 ◽  
pp. 111323 ◽  
Author(s):  
Catherine Torres de Almeida ◽  
Lênio Soares Galvão ◽  
Luiz Eduardo de Oliveira Cruz e Aragão ◽  
Jean Pierre Henry Balbaud Ometto ◽  
Aline Daniele Jacon ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Brazilian Amazon ◽  
Hyperspectral Data ◽  
Regression Algorithms ◽  
Biomass Modeling

Automatic tumor tissue classification of oncological esophageal resectates based on hyperspectral data

2019 ◽  
Author(s):  
M Maktabi ◽  
H Köhler ◽  
R Thieme ◽  
JP Takoh ◽  
SM Rabe ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Hyperspectral Data ◽  
Tissue Classification

AN AUTOMATIC APPROACH TO LOSSY COMPRESSION OF AVIRIS HYPERSPECTRAL DATA

2010 ◽  
Vol 69 (6) ◽  
pp. 537-563 ◽  
Author(s):  
N. N. Ponomarenko ◽  
M. S. Zriakhov ◽  
A. Kaarna
Keyword(s):  
Lossy Compression ◽  
Hyperspectral Data

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

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