Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm

This paper was supported by the National Natural Science Foundation of China (Grant No. 41371343 and Grant No. 41001255) and the scholarship provided by the China Scholarship Council (Grant No. 201308420290).

[1]  Yun Chen,et al.  Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Peter M. Atkinson,et al.  Mapping sub-pixel vector boundaries from remotely sensed images , 1996 .

[3]  Lalit Kumar,et al.  Comparative assessment of the measures of thematic classification accuracy , 2007 .

[4]  Pramod K. Varshney,et al.  Decision tree regression for soft classification of remote sensing data , 2005 .

[5]  Pramod K. Varshney,et al.  Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data , 2006, IEEE Geoscience and Remote Sensing Letters.

[6]  Giuseppe Zibordi,et al.  Uncertainties in Remote Sensing Reflectance From MODIS-Terra , 2012, IEEE Geoscience and Remote Sensing Letters.

[7]  Hugh G. Lewis,et al.  Super-resolution mapping using Hopfield Neural Network with panchromatic imagery , 2011 .

[8]  Soorathep Kheawhom,et al.  Modified genetic algorithm with sampling techniques for chemical engineering optimization , 2009 .

[9]  Yi Peng,et al.  Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data , 2014 .

[10]  Robert A. Schowengerdt,et al.  A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification , 1995, IEEE Trans. Geosci. Remote. Sens..

[11]  Graham Currie,et al.  Optimization of Transit Priority in the Transportation Network Using a Genetic Algorithm , 2011, IEEE Transactions on Intelligent Transportation Systems.

[12]  Guifeng Zhang,et al.  Uncertainty analysis of object location in multi-source remote sensing imagery classification , 2009 .

[13]  Shin Ishii,et al.  Superresolution with compound Markov random fields via the variational EM algorithm , 2009, Neural Networks.

[14]  Xuefeng Yan,et al.  A Fuzzy-based Adaptive Genetic Algorithm and Its Case Study in Chemical Engineering , 2011 .

[15]  Chein-I Chang,et al.  Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[16]  Susan Cuddy,et al.  A spatial framework for regional-scale flooding risk assessment , 2014 .

[17]  Reza Tavakkoli-Moghaddam,et al.  The use of a genetic algorithm for clustering the weighing station performance in transportation - A case study , 2011, Expert Syst. Appl..

[18]  Xiaoling Chen,et al.  Monitoring the dynamics of wetland inundation by random sets on multi-temporal images , 2011 .

[19]  DebKalyanmoy.  Multi-objective genetic algorithms , 1999 .

[20]  Frieke Van Coillie,et al.  Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium , 2007 .

[21]  Paul Aplin,et al.  Sub-pixel land cover mapping for per-field classification , 2001 .

[22]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[23]  Yun Chen,et al.  Sub-pixel flood inundation mapping from multispectral remotely sensed images based on discrete particle swarm optimization , 2015 .

[24]  Carlos Lopez-Martinez,et al.  Wetland inundation monitoring by the synergistic use of ENVISAT/ASAR imagery and ancilliary spatial data , 2013 .

[25]  Fernando Fernández-Rodríguez,et al.  Forecasting Financial Failure of Firms via Genetic Algorithms , 2013, Computational Economics.

[26]  Ben Gouweleeuw,et al.  Using MODIS for mapping flood events for use in hydrological and hydrodynamic models: Experiences so far , 2013 .

[27]  Mostafa Hajiaghaei-Keshteli,et al.  Genetic algorithms for coordinated scheduling of production and air transportation , 2010, Expert Syst. Appl..

[28]  X. Tong,et al.  Detection of urban sprawl using a genetic algorithm-evolved artificial neural network classification in remote sensing: a case study in Jiading and Putuo districts of Shanghai, China , 2010 .

[29]  E Le.  What are wetlands , 2006 .

[30]  Ming-Der Yang,et al.  A genetic algorithm (GA) based automated classifier for remote sensing imagery , 2007 .

[31]  Kenneth F. Reinschmidt,et al.  Construction scheduling using Genetic Algorithm based on Building Information Model , 2014, Expert Syst. Appl..

[32]  Shuai Li,et al.  Remote sensing of chlorophyll-a concentration for drinking water source using genetic algorithms (GA)-partial least square (PLS) modeling , 2012, Ecol. Informatics.

[33]  Valentyn Tolpekin,et al.  Markov random field based super-resolution mapping for identification of urban trees in VHR images , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[34]  Rouzbeh Shad,et al.  Uncertain spatial reasoning of environmental risks in GIS using genetic learning algorithms , 2012, Environmental Monitoring and Assessment.

[35]  Didier Sornette,et al.  Reverse Engineering Financial Markets with Majority and Minority Games Using Genetic Algorithms , 2010, Computational Economics.

[36]  Yun Chen,et al.  Linking inundation timing and extent to ecological response models using the Murray-Darling Basin Floodplain Inundation Model (MDB-FIM) , 2010 .

[37]  Liangpei Zhang,et al.  A new sub-pixel mapping algorithm based on a BP neural network with an observation model , 2008, Neurocomputing.

[38]  Robert De Wulf,et al.  Land cover mapping at sub-pixel scales using linear optimization techniques , 2002 .

[39]  Harun Kemal Ozturk,et al.  Forecasting total and industrial sector electricity demand based on genetic algorithm approach: Turkey case study , 2005 .

[40]  Rui Liu,et al.  Integration of remotely sensed inundation extent and high-precision topographic data for mapping inundation depth , 2014, 2014 The Third International Conference on Agro-Geoinformatics.

[41]  Tonny J. Oyana,et al.  Automatic cluster identification for environmental applications using the self-organizing maps and a new genetic algorithm , 2010 .

[42]  Xiaodong Li,et al.  A spatial–temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images , 2014 .

[43]  Marek Andrzej Krzeminski,et al.  Modeling Friction through the use of a Genetic Algorithm , 2003 .

[44]  Xiaodong Li,et al.  Super-Resolution Mapping of Forests With Bitemporal Different Spatial Resolution Images Based on the Spatial-Temporal Markov Random Field , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[45]  Koen C. Mertens,et al.  A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models , 2006 .

[46]  C. Ticehurst,et al.  An Evaluation of MODIS Daily and 8-day Composite Products for Floodplain and Wetland Inundation Mapping , 2013, Wetlands.

[47]  Jia Yu,et al.  Detecting floodplain inundation frequency using MODIS time-series imagery , 2012, 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics).

[48]  Susan Cuddy,et al.  Estimate of flood inundation and retention on wetlands using remote sensing and GIS , 2014 .

[49]  P. Atkinson.  Sub-pixel Target Mapping from Soft-classified, Remotely Sensed Imagery , 2005 .

[50]  Feng Qian,et al.  Dynamic optimization of chemical engineering problems using a control vector parameterization method with an iterative genetic algorithm , 2013 .

[51]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[52]  Lieven Verbeke,et al.  Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients , 2004 .

[53]  R. Colwell.  Remote sensing of the environment , 1980, Nature.

[54]  Yun Chen,et al.  DEM-based modification of pixel-swapping algorithm for enhancing floodplain inundation mapping , 2014 .

[55]  Jie Wang,et al.  [Hard and soft classification method of multi-spectral remote sensing image based on adaptive thresholds]. , 2013, Guang pu xue yu guang pu fen xi = Guang pu.

[56]  R. Balasubramanian,et al.  Optimization of India’s electricity generation portfolio using intelligent Pareto-search genetic algorithm , 2014 .

[57]  Kittipong Boonlong,et al.  Multi-objective genetic algorithms for solving portfolio optimization problems in the electricity market , 2014 .