Reflectance from a Sinusoidal Bottom and Discrimination of Water Types with Hyperspectral Data

2002 ◽  
Author(s):  
William Philpot
Keyword(s):  
Hyperspectral Data ◽  
Water Types

Deciphering Optical Water Types of Wetlands using multispectral Earth observation datasets

2020 ◽  
Author(s):  
Satyasri Allaka ◽  
Manudeo Singh ◽  
Rajiv Sinha
Keyword(s):  
Chlorophyll Content ◽  
Color Index ◽  
Coastal Wetland ◽  
Hyperspectral Data ◽  
Anthropogenic Factors ◽  
Optical Parameters ◽  
Spectral Indices ◽  
Water Types ◽  
Inorganic Matter ◽  
Sensitive Parameters

<p>Wetlands are important and highly productive ecosystems in a variety of geomorphic settings ranging from inland to coastal environments. Wetlands are very dynamic in nature and are driven by the water and sediment fluxes carried by the streamlets throughout the year. Wetlands are under tremendous pressure all over the world due to various natural and anthropogenic factors, and therefore, require an immediate attention for their conservation. The available studies on wetland have given much less importance to the internal dynamics of the wetlands, which is primarily driven by hydrology and Land Use Land Cover (LULC) changes. Here, we propose to use the Optical Water Types (OWTs) concept to understand the hydrodynamics within the wetland.</p><p>The OWTs are the aquatic counterpart of terrestrial LULC classification and can be created by clustering of optically sensitive parameters like chlorophyll content, turbidity, suspended organic and inorganic matter using remote sensing reflectance, absorption, and scattering parameters. The Forel Ule (FU) color index, a visual color comparison scale of water bodies ranging from blue to cola brown (1-21), used a similar idea but is fairly limited in scope. The hyperspectral datasets have distinct absorption and reflection spectrum for various optically sensitive parameters, and therefore, they are particularly suited for this work. However, the availability of the high-resolution hyperspectral data is very limited and hence this research explores the possibility of deciphering the OWTs using multispectral datasets.</p><p>A possible approach to create OWTs is using the spectral indices of the multispectral datasets which are sensitive to the optical parameters instead of using the FU color index as a single parameter. In this work, various spectral indices which are independent and highly sensitivity to chlorophyll content, turbidity, suspended organic and inorganic matter are identified using the principal component analysis. The OWT clusters are created using the iso-cluster unsupervised classification similar to the LULC classification but the spectral indices are taken into account instead of directly using the spectral bands of satellite datasets. In this work, the Sentinel – 2A and 2B datasets are used to create independent OWT clusters of the Chilika (a Coastal wetland, along the east coast of India covering an area of 1,165 km<sup>2</sup>) and Kaabar Tal (an inland wetland in north Bihar plains, India covering an area of 51 km<sup>2</sup>) using the supervised classification method. The developed framework is very simple and robust in nature but the only disadvantage is that the clusters are variable in the temporal context. However, the temporal variations can be integrated with the spatial analysis to understand the wetland dynamics in the context of both space and time.</p>

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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