Deep learning Using Physically-Informed Input Data for Wetland Identification
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Jonathan L. Goodall | Madhur Behl | Gina L. O'Neil | Linnea Saby | J. Goodall | Madhur Behl | Linnea Saby
[1] John F. O'Callaghan,et al. The extraction of drainage networks from digital elevation data , 1984, Comput. Vis. Graph. Image Process..
[2] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[3] Jae Ogilvie,et al. Mapping wetlands: A comparison of two different approaches for New Brunswick, Canada , 2007, Wetlands.
[4] T. Dahl,et al. Wetlands, status and trends in the conterminous United States, mid-1970's to mid-1980's , 1991 .
[5] Jae Ogilvie,et al. Topographic modelling of soil moisture conditions: a comparison and verification of two models , 2009 .
[6] Yong Wang,et al. Coastal wetland mapping combining multi-date SAR and LiDAR , 2013 .
[7] L. Band. Topographic Partition of Watersheds with Digital Elevation Models , 1986 .
[8] Rasim Latifovic,et al. Assessment of Convolution Neural Networks for Surficial Geology Mapping in the South Rae Geological Region, Northwest Territories, Canada , 2018, Remote. Sens..
[9] P. Arp,et al. Using the Cartographic Depth-to-Water Index to Locate Small Streams and Associated Wet Areas across Landscapes , 2012 .
[10] David G. Tarboton,et al. On the extraction of channel networks from digital elevation data , 1991 .
[11] P. Comeau,et al. Linking the Depth-to-Water Topographic Index to Soil Moisture on Boreal Forest Sites in Alberta , 2016 .
[12] P. Gong,et al. Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China , 2011 .
[13] Chaopeng Shen,et al. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists , 2017, Water Resources Research.
[14] Luís Torgo,et al. A Survey of Predictive Modeling on Imbalanced Domains , 2016, ACM Comput. Surv..
[15] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[16] Panagiotis Tsakalides,et al. Deep Learning for Multilabel Land Cover Scene Categorization Using Data Augmentation , 2019, IEEE Geoscience and Remote Sensing Letters.
[17] Michele Volpi,et al. Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[18] Qingxi Tong,et al. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance , 2014 .
[19] Helena Mitasova,et al. Efficient extraction of drainage networks from massive, radar-based elevation models with least cost path search , 2011 .
[20] I. Moore,et al. Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .
[21] T. Warner,et al. Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects , 2011 .
[22] Joseph F. Knight,et al. Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota , 2013, Remote. Sens..
[23] Bo Du,et al. Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art , 2016, IEEE Geoscience and Remote Sensing Magazine.
[24] Keith C. Pelletier,et al. Wetland Mapping in the Upper Midwest United States: An Object-Based Approach Integrating Lidar and Imagery Data , 2014 .
[25] Yuhong He,et al. An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery ☆ , 2015 .
[26] E. Foufoula‐Georgiou,et al. Automatic geomorphic feature extraction from lidar in flat and engineered landscapes , 2011 .
[27] Aaron A. Berg,et al. Evaluating DEM conditioning techniques, elevation source data, and grid resolution for field-scale hydrological parameter extraction , 2016 .
[28] V. Klemas. Remote Sensing of Wetlands: Case Studies Comparing Practical Techniques , 2011 .
[29] Brian Brisco,et al. Semi-Automated Surface Water Detection with Synthetic Aperture Radar Data: A Wetland Case Study , 2017, Remote. Sens..
[30] Doerthe Tetzlaff,et al. A comparison of wetness indices for the prediction of observed connected saturated areas under contrasting conditions , 2014 .
[31] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[32] Mohamad Ali-Dib,et al. Lunar crater identification via deep learning , 2018, Icarus.
[33] Gina L. O'Neil,et al. Effects of LiDAR DEM Smoothing and Conditioning Techniques on a Topography‐Based Wetland Identification Model , 2019, Water Resources Research.
[34] K. Millard,et al. Wetland mapping with LiDAR derivatives, SAR polarimetric decompositions, and LiDAR–SAR fusion using a random forest classifier , 2013 .
[35] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] N. Davidson. How much wetland has the world lost? Long-term and recent trends in global wetland area , 2014 .
[37] Bertrand Le Saux,et al. Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.
[38] Peijun Du,et al. A review of supervised object-based land-cover image classification , 2017 .
[39] In-Young Yeo,et al. Topographic Metrics for Improved Mapping of Forested Wetlands , 2013, Wetlands.
[40] Junfeng Zhu,et al. Applying a weighted random forests method to extract karst sinkholes from LiDAR data , 2016 .
[41] Yun Zhang,et al. Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[42] Yun Zhang,et al. Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery , 2018, Remote. Sens..
[43] K. Todd,et al. Automated discrimination of upland and wetland using terrain derivatives , 2007 .
[44] R. Bras,et al. A physically-based method for removing pits in digital elevation models , 2007 .
[45] J. Carlin,et al. Bias, prevalence and kappa. , 1993, Journal of clinical epidemiology.
[46] Gina L. O'Neil,et al. Evaluating the potential for site-specific modification of LiDAR DEM derivatives to improve environmental planning-scale wetland identification using Random Forest classification , 2018 .
[47] S. K. Jenson,et al. Extracting topographic structure from digital elevation data for geographic information-system analysis , 1988 .
[48] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Jie Tian,et al. Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China , 2016, Remote. Sens..
[50] Aaron J. Smith,et al. A Semi-Automated, Multi-Source Data Fusion Update of a Wetland Inventory for East-Central Minnesota, USA , 2015, Wetlands.
[51] John B. Lindsay,et al. Efficient hybrid breaching‐filling sink removal methods for flow path enforcement in digital elevation models , 2016 .
[52] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[53] Peter K. Haff,et al. Microtopography as an indicator of modern hillslope diffusivity in arid terrain , 1997 .
[54] Jing Li,et al. A Review of Wetland Remote Sensing , 2017, Sensors.
[55] Ronald Kemker,et al. Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.
[56] P. Arp,et al. Modelling and mapping topographic variations in forest soils at high resolution: A case study , 2011 .
[57] Larry S. Davis,et al. Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[58] J. Lindsay,et al. Removal of artifact depressions from digital elevation models: towards a minimum impact approach , 2005 .
[59] Shaowen Wang,et al. A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[60] Jae-Dong Jang,et al. Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea , 2018, GIScience & Remote Sensing.
[61] Kuolin Hsu,et al. Improving Precipitation Estimation Using Convolutional Neural Network , 2019, Water Resources Research.
[62] Sabri Boughorbel,et al. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric , 2017, PloS one.
[63] Guillermo Sapiro,et al. A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths , 2010 .
[64] Yang Wang,et al. Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..
[65] K. Beven,et al. A physically based, variable contributing area model of basin hydrology , 1979 .
[66] P. Holmgren. Multiple flow direction algorithms for runoff modelling in grid based elevation models: An empirical evaluation , 1994 .
[67] Jitendra Malik,et al. Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[68] Huimin Yan,et al. A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China , 2018, Remote. Sens..
[69] Curt H. Davis,et al. Fusion of Deep Convolutional Neural Networks for Land Cover Classification of High-Resolution Imagery , 2017, IEEE Geoscience and Remote Sensing Letters.
[70] Uwe Stilla,et al. Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection , 2016, ISPRS Journal of Photogrammetry and Remote Sensing.
[71] Iryna Dronova,et al. Object-Based Image Analysis in Wetland Research: A Review , 2015, Remote. Sens..
[72] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[73] Paola Passalacqua,et al. GeoNet: An open source software for the automatic and objective extraction of channel heads, channel network, and channel morphology from high resolution topography data , 2016, Environ. Model. Softw..
[74] Paola Passalacqua,et al. Testing space‐scale methodologies for automatic geomorphic feature extraction from lidar in a complex mountainous landscape , 2010 .
[75] Stacy L. Ozesmi,et al. Satellite remote sensing of wetlands , 2002, Wetlands Ecology and Management.
[76] Thomas Serre,et al. A quantitative theory of immediate visual recognition. , 2007, Progress in brain research.
[77] Bertrand Le Saux,et al. Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks , 2016, ACCV.
[78] P. Arp,et al. Evaluating digital terrain indices for soil wetness mapping – a Swedish case study , 2014 .
[79] T. Carlson,et al. On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .
[80] Jing Huang,et al. DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[81] Koreen Millard,et al. On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping , 2015, Remote. Sens..
[82] Tao Liu,et al. Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system , 2018 .
[83] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[84] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.