scholarly journals Teams Invited to Test Coastal Hyperspectral Imaging Algorithms

Eos ◽  
2019 ◽  
Vol 100 ◽  
Author(s):  
Margaret McManus ◽  
Eric Hochberg
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Hyperspectral Remote Sensing ◽  
Inland Waters ◽  
Imaging Algorithms

Hyperspectral Remote Sensing of Coastal and Inland Waters Webinar; 28 May 2019

scholarly journals An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines

2016 ◽  
Vol 5 (2) ◽  
pp. 85 ◽  
Author(s):  
Alpana Shukla ◽  
Rajsi Kot
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Spatial Information ◽  
Coastal Zone Management ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Data Sets ◽  
Imaging Data ◽  
Advantages And Disadvantages

<div><p><em>Recent advances in remote sensing and geographic information has opened new directions for the development of hyperspectral sensors. Hyperspectral remote sensing, also known as imaging spectroscopy is a new technology. Hyperspectral imaging is currently being investigated by researchers and scientists for the detection and identification of vegetation, minerals, different objects and background.</em><em> Hyperspectral remote sensing combines imaging and spectroscopy in a single system which often includes large data sets and requires new processing methods. Hyperspectral data sets are generally made of about 100 to 200 spectral bands of relatively narrow bandwidths (5-10 nm), whereas, multispectral data sets are usually composed of about 5 to 10 bands of relatively large bandwidths (70-400 nm). Hyperspectral imagery is collected as a data cube with spatial information collected in the X-Y plane, and spectral information represented in the Z-direction. </em><em>Hyperspectral remote sensing is applicable in many different disciplines. It was originally developed for mining and geology; it has now spread into fields such as agriculture and forestry, ecology, coastal zone management, geology and mineral exploration. This paper presents an overview of hyperspectral imaging, data exploration and analysis, applications in various disciplines, advantages and disadvantages and future aspects of the technique.</em></p></div>

UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring

2021 ◽  
Vol 13 (20) ◽  
pp. 4069
Author(s):  
Hong Liu ◽  
Tao Yu ◽  
Bingliang Hu ◽  
Xingsong Hou ◽  
Zhoufeng Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Hyperspectral Imaging ◽  
Water Surface ◽  
Water Quality Monitoring ◽  
Hyperspectral Remote Sensing ◽  
Quality Monitoring ◽  
Chl A ◽  
Sensing System ◽  
Laboratory Calibration

Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint air–ground water quality monitoring activity were performed. The hyperspectral imaging remote sensing system of UAV comprised a light and small UAV platform, spectral scanning hyperspectral imager, and data acquisition and control unit. The spectral principle of the hyperspectral imager is based on the new high-performance acousto-optic tunable (AOTF) technology. During laboratory calibration, the spectral calibration of the imaging spectrometer and image preprocessing in data acquisition were completed. In the UAV air–ground joint experiment, combined with the typical water bodies of the Yangtze River mainstream, the Three Gorges demonstration area, and the Poyang Lake demonstration area, the hyperspectral data cubes of the corresponding water areas were obtained, and geometric registration was completed. Thus, a large field-of-view mosaic and water radiation calibration were realized. A chlorophyl-a (Chl-a) sensor was used to test the actual water control points, and 11 traditional Chl-a sensitive spectrum selection algorithms were analyzed and compared. A random forest algorithm was used to establish a prediction model of water surface spectral reflectance and water quality parameter concentration. Compared with the back propagation neural network, partial least squares, and PSO-LSSVM algorithms, the accuracy of the RF algorithm in predicting Chl-a was significantly improved. The determination coefficient of the training samples was 0.84; root mean square error, 3.19 μg/L; and mean absolute percentage error, 5.46%. The established Chl-a inversion model was applied to UAV hyperspectral remote sensing images. The predicted Chl-a distribution agreed with the field observation results, indicating that the UAV-borne hyperspectral remote sensing water quality monitoring system based on AOTF is a promising remote sensing imaging spectral analysis tool for water.

scholarly journals Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters

2015 ◽  
Vol 167 ◽  
pp. 196-205 ◽  
Author(s):  
Raphael M. Kudela ◽  
Sherry L. Palacios ◽  
David C. Austerberry ◽  
Emma K. Accorsi ◽  
Liane S. Guild ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Cyanobacterial Blooms ◽  
Inland Waters

scholarly journals Advances in hyperspectral remote sensing I: The visible Fourier transform hyperspectral imager

2015 ◽  
Author(s):  
J. Rafert
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Research And Development ◽  
Hyperspectral Imaging ◽  
Hyperspectral Remote Sensing ◽  
Optical Engineering ◽  
Successful Launch ◽  
Modulated Imaging ◽  
Institute Of Technology ◽  
New Applications

We discuss early hyperspectral research and development activities during the 1990s that led to the deployment of aircraft and satellite payloads whose heritage was based on the use of visible, spatially modulated, imaging Fourier transform spectrometers, beginning with early experiments at the Florida Institute of Technology, through successful launch and deployment of the Visible Fourier Transform Hyperspectral Imager on MightySat II.1 on 19 July 2000. In addition to a brief chronological overview, we also discuss several of the most interesting optical engineering challenges that were addressed over this timeframe, present some as-yet un-exploited features of field-widened (slit-less) SMIFTS instruments, and present some images from ground-based, aircraft-based and satellite-based instruments that helped provide the impetus for the proliferation and development of entire new families of instruments and countless new applications for hyperspectral imaging.

scholarly journals Inland Waters Suspended Solids Concentration Retrieval Based on PSO-LSSVM for UAV-Borne Hyperspectral Remote Sensing Imagery

2019 ◽  
Vol 11 (12) ◽  
pp. 1455 ◽  
Author(s):  
Lifei Wei ◽  
Can Huang ◽  
Yanfei Zhong ◽  
Zhou Wang ◽  
Xin Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Least Squares ◽  
Empirical Model ◽  
Suspended Solids ◽  
Hyperspectral Remote Sensing ◽  
Support Vector ◽  
Inland Waters ◽  
Important Indicator ◽  
Solids Concentration ◽  
Semi Empirical

Suspended solids concentration (SSC) is an important indicator of the degree of water pollution. However, when using an empirical or semi-empirical model adapted to some of the inland waters to estimate SSC on unmanned aerial vehicle (UAV)-borne hyperspectral images, the accuracy is often not sufficient. Thus, in this study, we attempted to use the particle swarm optimization (PSO) algorithm to find the optimal parameters of the least-squares support vector machine (LSSVM) model for the quantitative inversion of SSC. A reservoir and a polluted riverway were selected as the study areas. The spectral data of the 36-point and 29-point 400–900 nm wavelength range on the UAV-borne images were extracted. Compared with the semi-empirical model, the random forest (RF) algorithm and the competitive adaptive reweighted sampling (CARS) algorithm combined with partial least squares (PLS), the accuracy of the PSO-LSSVM algorithm in predicting the SSC was significantly improved. The training samples had a coefficient of determination ( R 2 ) of 0.98, a root mean square error (RMSE) of 0.68 mg/L, and a mean absolute percentage error (MAPE) of 12.66% at the reservoir. For the polluted riverway, PSO-LSSVM also performed well. Finally, the established SSC inversion model was applied to UAV-borne hyperspectral remote sensing (HRS) images. The results confirmed that the distribution of the predicted SSC was consistent with the observed results in the field, which proves that PSO-LSSVM is a feasible approach for the SSC inversion of UAV-borne HRS images.

Remote sensing of gases by hyperspectral imaging: algorithms and results of field measurements

2012 ◽  
Author(s):  
Samer Sabbah ◽  
Peter Rusch ◽  
Jens Eichmann ◽  
Jörn-Hinnrich Gerhard ◽  
Roland Harig
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Field Measurements ◽  
Imaging Algorithms
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