Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery
Authors
Keywords
Optically inactive water quality parameters, Airborne hyperspectral imagery, Deep learning based regression
Journal
ENVIRONMENTAL POLLUTION
Volume 286, Issue -, Pages 117534
Publisher
Elsevier BV
Online
2021-06-06
DOI
10.1016/j.envpol.2021.117534
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification
- (2019) Zhiqiang Gong et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Regionally and Locally Adaptive Models for Retrieving Chlorophyll-a Concentration in Inland Waters From Remotely Sensed Multispectral and Hyperspectral Imagery
- (2019) Min Xu et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Caps-TripleGAN: GAN-Assisted CapsNet for Hyperspectral Image Classification
- (2019) Xue Wang et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Determination of optically inactive water quality variables using Landsat 8 data: A case study in Geshlagh reservoir affected by agricultural land use
- (2019) Taleb Vakili et al. JOURNAL OF CLEANER PRODUCTION
- A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery
- (2019) JongCheol Pyo et al. REMOTE SENSING OF ENVIRONMENT
- Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN)
- (2019) Michal Segal-Rozenhaimer et al. REMOTE SENSING OF ENVIRONMENT
- An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze
- (2019) Shaohua Lei et al. SCIENCE OF THE TOTAL ENVIRONMENT
- High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery
- (2018) Jong Pyo et al. Remote Sensing
- Estimation of Colored Dissolved Organic Matter From Landsat-8 Imagery for Complex Inland Water: Case Study of Lake Huron
- (2017) Jiang Chen et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Hyperspectral Image Classification Using Deep Pixel-Pair Features
- (2017) Wei Li et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework
- (2017) Essam Sharaf El Din et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Remote sensing estimation of colored dissolved organic matter (CDOM) in optically shallow waters
- (2017) Jiwei Li et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Hyperspectral image reconstruction by deep convolutional neural network for classification
- (2017) Yunsong Li et al. PATTERN RECOGNITION
- Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan
- (2017) Zuomin Wang et al. Remote Sensing
- A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques
- (2016) Mohammad Gholizadeh et al. SENSORS
- Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems?
- (2015) Bryan W. Brooks et al. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY
- A novel multi-parameter support vector machine for image classification
- (2015) Ce Zhang et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Improved capabilities of the Chinese high-resolution remote sensing satellite GF-1 for monitoring suspended particulate matter (SPM) in inland waters: Radiometric and spatial considerations
- (2015) Jian Li et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Remote sensing of inland waters: Challenges, progress and future directions
- (2015) Stephanie C.J. Palmer et al. REMOTE SENSING OF ENVIRONMENT
- Remote sensing of selected water-quality indicators with the hyperspectral imager for the coastal ocean (HICO) sensor
- (2014) Darryl J. Keith et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Using in situ and Satellite Hyperspectral Data to Estimate the Surface Suspended Sediments Concentrations in the Pearl River Estuary
- (2013) Qianguo Xing et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Support vector regression and synthetically mixed training data for quantifying urban land cover
- (2013) Akpona Okujeni et al. REMOTE SENSING OF ENVIRONMENT
- Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model
- (2013) Kaishan Song et al. REMOTE SENSING OF ENVIRONMENT
- Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: The Mississippi River and its tributaries in Minnesota
- (2012) Leif G. Olmanson et al. REMOTE SENSING OF ENVIRONMENT
- LIBSVM
- (2012) Chih-Chung Chang et al. ACM Transactions on Intelligent Systems and Technology
- Support vector machines in water quality management
- (2011) Kunwar P. Singh et al. ANALYTICA CHIMICA ACTA
- Hyperspectral Remote Sensing of Total Phosphorus (TP) in Three Central Indiana Water Supply Reservoirs
- (2011) Kaishan Song et al. WATER AIR AND SOIL POLLUTION
- Support Vector Machines for classification and regression
- (2009) Richard G. Brereton et al. ANALYST
- A Unified Model for Remotely Estimating Chlorophyll a in Lake Taihu, China, Based on SVM and In Situ Hyperspectral Data
- (2009) Deyong Sun et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation
- (2008) Anatoly A. Gitelson et al. REMOTE SENSING OF ENVIRONMENT
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now