Channel bar feature extraction for a mining-contaminated river using high-spatial multispectral remote-sensing imagery
暂无分享,去创建一个
Qunying Huang | Caixia Wang | R. Pavlowsky | Qunying Huang | Charles Chang | Caixia Wang | Robert T. Pavlowsky | Charles Chang
[1] Priyakant Sinha,et al. Mapping salt-marsh land-cover vegetation using high-spatial and hyperspectral satellite data to assist wetland inventory , 2014 .
[2] B. Diekkrüger,et al. Floodplain wetland mapping in the White Volta River Basin of Ghana , 2015 .
[3] Curt H. Davis,et al. A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..
[4] R. Pavlowsky,et al. Spatial Patterns of Channel Instability Along an Ozark River, Southwest Missouri , 2011 .
[5] Raul Queiroz Feitosa,et al. Modeling alternatives for fuzzy Markov chain-based classification of multitemporal remote sensing data , 2011, Pattern Recognit. Lett..
[6] Chih-Jen Lin,et al. Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..
[7] Arno Schäpe,et al. Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .
[8] K. R. Long,et al. Database of significant deposits of gold, silver, copper, lead, and zinc in the United States , 1998 .
[9] Guobin Zhu,et al. Classification using ASTER data and SVM algorithms;: The case study of Beer Sheva, Israel , 2002 .
[10] M. Tubino,et al. Free–forced interactions in developing meanders and suppression of free bars , 1990, Journal of Fluid Mechanics.
[11] Josef Strobl,et al. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS , 2001 .
[12] D. Lu,et al. A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon , 2014 .
[13] T. Lisle. Applied River Morphology , 1998 .
[14] Caiyun Zhang,et al. Object-based Vegetation Mapping in the Kissimmee River Watershed Using HyMap Data and Machine Learning Techniques , 2013, Wetlands.
[15] P. Gong,et al. Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .
[16] Thomas Blaschke,et al. Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[17] Biswajeet Pradhan,et al. A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images , 2014 .
[18] D. Montgomery. Watershed Assessment of River Stability and Sediment Supply ( WARSSS ) , 2008 .
[19] Yuhong He,et al. An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery ☆ , 2015 .
[20] P. J. Edwards,et al. A conceptual model of vegetation dynamics on gravel bars of a large Alpine river , 1999, Wetlands Ecology and Management.
[21] Fernando Bação,et al. Self-organizing Maps as Substitutes for K-Means Clustering , 2005, International Conference on Computational Science.
[22] Lee H. MacDonald,et al. DIAGNOSTIC APPROACH TO STREAM CHANNEL ASSESSMENT AND MONITORING 1 , 2002 .
[23] U. Benz,et al. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .
[24] Volker Walter,et al. Object-based classification of remote sensing data for change detection , 2004 .
[25] Martin Herold,et al. Spectral resolution requirements for mapping urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..
[26] D. Hannah,et al. Riparian vegetation and island formation along the gravel‐bed Fiume Tagliamento, Italy , 2001 .
[27] D. Gilvear,et al. Channel dynamics and geomorphic variability as controls on gravel bar vegetation; River Tummel, Scotland , 2006 .
[28] David A. Landgrebe,et al. The development of a spectral-spatial classifier for earth observational data , 1980, Pattern Recognit..
[29] A. Rango,et al. Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .
[30] G. Zolezzi,et al. Downstream and upstream influence in river meandering. Part 2. Planimetric development , 2001, Journal of Fluid Mechanics.
[31] Gregory S. Biging,et al. An iterative classification approach for mapping natural resources from satellite imagery , 1996 .
[32] Claudia Notarnicola,et al. Identification of orchards and vineyards with different texture-based measurements by using an object-oriented classification approach , 2011, Int. J. Geogr. Inf. Sci..
[33] Jungho Im,et al. Support vector machines in remote sensing: A review , 2011 .
[34] L. S. Galvão,et al. Potential of multispectral and hyperspectral data to detect saline-exposed soils in Brazil , 2015 .
[35] Martin Charlton,et al. Application of airborne LiDAR in river environments: the River Coquet, Northumberland, UK , 2003 .
[36] John R. Jensen,et al. Object‐based change detection using correlation image analysis and image segmentation , 2008 .
[37] R. Pavlowsky,et al. Historical Disturbance and Contemporary Floodplain Development along an Ozark River, Southwest Missouri , 2011 .
[38] Zhilei Lin,et al. A support vector machine classifier based on a new kernel function model for hyperspectral data , 2016 .
[39] Thomas Blaschke,et al. Image Segmentation Methods for Object-based Analysis and Classification , 2004 .
[40] Le Wang,et al. An object-based SVM method incorporating optimal segmentation scale estimation using Bhattacharyya Distance for mapping salt cedar (Tamarisk spp.) with QuickBird imagery , 2015 .
[41] Jean-Pierre Gambotto,et al. A new approach to combining region growing and edge detection , 1993, Pattern Recognit. Lett..
[42] B. Koch,et al. Landscape structure assessment with image grey‐values and object‐based classification at three spatial resolutions , 2005 .
[43] Morris Goldberg,et al. Hierarchy in Picture Segmentation: A Stepwise Optimization Approach , 1989, IEEE Trans. Pattern Anal. Mach. Intell..
[44] S. W. Anderson,et al. Channel change and bed-material transport in the Umpqua River basin, Oregon , 2010 .
[45] S. Ustin. Remote sensing for natural resource management and environmental monitoring , 2004 .
[46] Thomas Blaschke,et al. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms , 2010, Int. J. Appl. Earth Obs. Geoinformation.
[47] Patrice E. Carbonneau,et al. Aerial photosieving of exposed gravel bars for the rapid calibration of airborne grain size maps , 2010 .
[48] D. Raff,et al. Assessing the ability of airborne LiDAR to map river bathymetry , 2008 .
[49] K. Navulur. Multispectral Image Analysis Using the Object-Oriented Paradigm , 2006 .
[50] Thomas Blaschke,et al. Object based image analysis for remote sensing , 2010 .
[51] Aaron E. Maxwell,et al. Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation , 2014 .
[52] Marcelo H. García,et al. Experiments in a high‐amplitude Kinoshita meandering channel: 2. Implications of bend orientation on bed morphodynamics , 2009 .
[53] R. Platt,et al. An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification , 2008 .
[54] Robert B. Jacobson,et al. Relations among geology, physiography, land use, and stream habitat conditions in the Buffalo and Current River Systems, Missouri and Arkansas , 2001 .
[55] Russell G. Congalton,et al. Assessing the accuracy of remotely sensed data : principles and practices , 1998 .
[56] Zhigang Pan,et al. Performance Assessment of High Resolution Airborne Full Waveform LiDAR for Shallow River Bathymetry , 2015, Remote. Sens..
[57] S. Myint. A New Framework for Effective Urban Land Use and Land Cover Classification: A Wavelet Approach , 2006 .
[58] David Gilvear,et al. The use of remotely sensed data to detect channel hydromorphology; River Tummel, Scotland , 2004 .
[59] AUTOMATIC DETECTION OF GRAVEL BARS IN A RIVER CHANNEL FROM AIRBORNE LIDAR-DERIVED DTM , 2013 .
[60] Stuart R. Phinn,et al. Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach , 2011, Remote. Sens..
[61] Geoffrey J. Hay,et al. An object-specific image-texture analysis of H-resolution forest imagery☆ , 1996 .
[62] R. Jacobson,et al. Historical land-use changes and potential effects on stream disturbance in the Ozark Plateaus, Missouri , 1994 .
[63] J. L. Moigne,et al. Refining image segmentation by integration of edge and region data , 1992, IEEE Trans. Geosci. Remote. Sens..
[64] J. Brasington,et al. Monitoring and modelling morphological change in a braided gravel‐bed river using high resolution GPS‐based survey , 2000 .
[65] Ute Beyer,et al. Remote Sensing And Image Interpretation , 2016 .
[66] R. Jacobson,et al. Gravel sediment routing from widespread, low‐ intensity landscape disturbance, Current River Basin, Missouri , 1999 .
[67] John R. Jensen,et al. Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .
[68] Rolf Adams,et al. Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[69] P. Fisher. The pixel: A snare and a delusion , 1997 .
[70] Mahendra Kumar,et al. PRACTICAL ASPECTS OF IKONOS IMAGERY FOR MAPPING , 2001 .
[71] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[72] C. Brodley,et al. Decision tree classification of land cover from remotely sensed data , 1997 .
[73] J. Hooke,et al. Channel bar dynamics on multi‐decadal timescales in an active meandering river , 2011 .
[74] Xiaomin Qiu,et al. Incorporating road and parcel data for object-based classification of detailed urban land covers from NAIP images , 2014 .
[75] Thomas Blaschke,et al. New contextual approaches using image segmentation for objectbased classification , 2004 .
[76] Stanley A. Schumm,et al. River Variability and Complexity: Index , 2005 .
[77] Antonio J. Plaza,et al. Subspace-Based Support Vector Machines for Hyperspectral Image Classification , 2015, IEEE Geoscience and Remote Sensing Letters.
[78] Anthony M. Filippi,et al. River-flow boundary delineation from digital aerial photography and ancillary images using Support Vector Machines , 2013 .
[79] Peter R. Wilcock,et al. Toward a practical method for estimating sediment‐transport rates in gravel‐bed rivers , 2001 .
[80] James D. Kiser,et al. Aerial Photography and Image Interpretation: Paine/Aerial Photography Image Interpretation 3E , 2012 .
[81] Douglas E. Smith,et al. River Variability and Complexity: Index , 2005 .