Journal Article10.1016/J.RSE.2012.11.023
Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: the Mississippi River and its tributaries in Minnesota.
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TL;DR: In this paper, the spectral characteristics that distinguish waters dominated by several inherent optical properties (IOPs) were used to develop models to map water quality characteristics in optically complex waters.
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About: This article is published in Remote Sensing of Environment. The article was published on 15 Mar 2013. The article focuses on the topics: Total suspended solids & Suspended solids.
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Citations
Deep learning classifiers for hyperspectral imaging: A review
TL;DR: A comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature is provided, providing an exhaustive comparison of the discussed techniques.
903
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
TL;DR: Wang et al. as mentioned in this paper proposed a cascaded RNN model using gated recurrent units (GRUs) to explore the redundant and complementary information of hyperspectral images (HSIs).
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
TL;DR: Wang et al. as discussed by the authors proposed a sequence-based recurrent neural network (RNN) for hyperspectral image classification, which makes use of a newly proposed activation function, parametric rectified tanh (PRetanh), instead of the popular tanh or rectified linear unit.
Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing
Vasit Sagan,Kyle Peterson,Maitiniyazi Maimaitijiang,Paheding Sidike,Paheding Sidike,John J. Sloan,Benjamin A. Greeling,Samar Maalouf,Craig D. Adams +8 more
TL;DR: In this paper, a review of water quality remote sensing systems and their limitations is presented, and the authors conclude that anomaly detection utilizing multi-sensor data fusion and virtual constellation in cloud computing is the most promising means for predicting impending water pollution outbreaks such as algal blooms.
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An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities
Christine Lee,Morgan L. Cable,Simon J. Hook,Robert O. Green,Susan L. Ustin,Daniel Mandl,Elizabeth M. Middleton +6 more
TL;DR: The NASA Hyperspectral InfraRed Imager (HyspIRI) as mentioned in this paper is comprised of a visible to short-wavelength infrared (VSWIR) imaging spectrometer and a thermal infrared (TIR) multispectral imager, together with an Intelligent Payload Module (IPM) for onboard processing and rapid downlink of selected data.
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References
Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop
G. Vane,A. F. H. Goetz +1 more
- 15 Jun 1985
TL;DR: The Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on April 8 to 10, 1985 as discussed by the authors, where 92 people who heard reports on 30 investigations currently under way using AIS data that have been collected over the past two years.
50
ICA mixture model based unsupervised classification of hyperspectral imagery
C. A. Shah,Manoj K. Arora,Stefan A. Robila,Pramod K. Varshney +3 more
- 16 Oct 2002
TL;DR: The results demonstrate that the ICAMM significantly outperforms the K-means algorithm for land cover classification of hyperspectral imagery implemented on reduced data sets.
37
Historical trends affecting accumulation of sediment and phosphorus in Lake Pepin, upper Mississippi River, USA
David J. Mulla,Adam C. Sekely +1 more
TL;DR: In this article, a study was conducted to collect historical land use information that would help explain the historical patterns in accumulation of sediment and phosphorus in Lake Pepin documented by Engstrom et al. They found that sediment losses from the Minnesota River basin are significantly correlated with historical increases in river flows, row crop production acreage and basin population.
23
Nitrogen input to the Gulf of Mexico.
TL;DR: The mean annual N flux has changed little since the early 1980s, but large year-to-year variations in N flux occur because of variations in precipitation.
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