Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network
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Hua Liu | Xuejian Li | Huaqiang Du | Fangjie Mao | Guomo Zhou | Shaobai He | Meng Zhang | Yanxin Xu | Dien Zhu | Zihao Huang | Xin Luo | H. Du | Guomo Zhou | Xin Luo | Fangjie Mao | Xuejian Li | Di'en Zhu | Meng Zhang | Zihao Huang | Shaobai He | Hua Liu | Yanxin Xu
[1] Gui-Song Xia,et al. Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models , 2018 .
[2] Lingfeng Wang,et al. Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.
[3] Manfred F. Buchroithner,et al. A Synergetic Analysis of Sentinel-1 and -2 for Mapping Historical Landslides Using Object-Oriented Random Forest in the Hyrcanian Forests , 2019, Remote. Sens..
[4] Peter Caccetta,et al. ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.
[5] Yi Zhang,et al. SSDC-DenseNet: A Cost-Effective End-to-End Spectral-Spatial Dual-Channel Dense Network for Hyperspectral Image Classification , 2019, IEEE Access.
[6] Tianyu Zhao,et al. Remote sensing image classification based on semi-supervised adaptive interval type-2 fuzzy c-means algorithm , 2019, Comput. Geosci..
[7] Bin Chen,et al. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. , 2019, Science bulletin.
[8] William J. Emery,et al. Object-Based Convolutional Neural Network for High-Resolution Imagery Classification , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[9] Carlo Gatta,et al. Unsupervised Deep Feature Extraction for Remote Sensing Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[10] Peng Yue,et al. A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images , 2020, Int. J. Appl. Earth Obs. Geoinformation.
[11] Xin Pan,et al. An object-based convolutional neural network (OCNN) for urban land use classification , 2018, Remote Sensing of Environment.
[12] Jon Atli Benediktsson,et al. Land-Cover Mapping by Markov Modeling of Spatial–Contextual Information in Very-High-Resolution Remote Sensing Images , 2013, Proceedings of the IEEE.
[13] Feng Zhang,et al. Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands. , 2018 .
[14] D. Nowak,et al. A review of urban forest modeling: Implications for management and future research , 2019, Urban Forestry & Urban Greening.
[15] Tao Liu,et al. Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification , 2018 .
[16] Michael Weinmann,et al. SEMANTIC SEGMENTATION OF AERIAL IMAGERY VIA MULTI-SCALE SHUFFLING CONVOLUTIONAL NEURAL NETWORKS WITH DEEP SUPERVISION , 2018, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
[17] Christoph Straub,et al. Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data , 2019, Remote. Sens..
[18] Bo Du,et al. Kernel Slow Feature Analysis for Scene Change Detection , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[19] Zhaohui Wu,et al. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Land-Use Classification Using Taxi GPS Traces , 2022 .
[20] Bo Huang,et al. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery , 2018, Remote Sensing of Environment.
[21] Xinchang Zhang,et al. Developing a multi-filter convolutional neural network for semantic segmentation using high-resolution aerial imagery and LiDAR data , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[22] Neil Flood,et al. Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[23] Chunjiang Liu,et al. Mapping aboveground biomass and carbon in Shanghai's urban forest using Landsat ETM+ and inventory data , 2020 .
[24] Runhe Shi,et al. Automatic Selection of Optimal Segmentation Scale of High-resolution Remote Sensing Images , 2013 .
[25] J. Macgregor,et al. Image texture analysis: methods and comparisons , 2004 .
[26] Kun Zhu,et al. Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[27] Neal W. Aven,et al. Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data , 2017 .
[28] R. Pu,et al. A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species , 2012 .
[29] Wenjuan Shen,et al. Assessing spatio-temporal changes in forest cover and fragmentation under urban expansion in Nanjing, eastern China, from long-term Landsat observations (1987–2017) , 2020, Applied Geography.
[30] Jing Sun,et al. Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data , 2020, Remote. Sens..
[31] Ning Han,et al. Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China , 2018, Remote. Sens..
[32] Shihong Du,et al. Learning multiscale and deep representations for classifying remotely sensed imagery , 2016 .
[33] Xuejian Li,et al. Very High Resolution Remote Sensing Imagery Classification Using a Fusion of Random Forest and Deep Learning Technique—Subtropical Area for Example , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[34] André Stumpf,et al. bject-oriented mapping of urban trees using Random Forest lassifiers , 2013 .
[35] James R. Anderson,et al. A land use and land cover classification system for use with remote sensor data , 1976 .
[36] Wenju Wang,et al. A Fast Dense Spectral-Spatial Convolution Network Framework for Hyperspectral Images Classification , 2018, Remote. Sens..
[37] Francisco Rowe,et al. A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning , 2019, Remote. Sens..
[38] Brian K. Gelder,et al. Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution , 2020 .
[39] Xiaoli Li,et al. Hyperspectral remote sensing image classification using three-dimensional-squeeze-and-excitation-DenseNet (3D-SE-DenseNet) , 2020 .
[40] Giles M. Foody,et al. Good practices for estimating area and assessing accuracy of land change , 2014 .
[41] Hamami Latifa,et al. Spatio Temporal Analysis of Vegetation by Vegetation Indices from Multi-dates Satellite Images: Application to a Semi Arid Area in ALGERIA , 2013 .
[42] Xiaoyan Sun,et al. Synergistic use of Landsat TM and SPOT5 imagery for object-based forest classification , 2014 .
[43] Meng Zhang,et al. Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests , 2020, ISPRS Int. J. Geo Inf..
[44] Christiane Schmullius,et al. Object-based land cover mapping and comprehensive feature calculation for an automated derivation of urban structure types at block level , 2014 .
[45] Meng Zhang,et al. Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model , 2019, Forests.
[46] Bin Zhang,et al. Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images , 2020, Remote Sensing of Environment.
[47] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[48] R. Pu,et al. Mapping urban tree species by integrating multi-seasonal high resolution pléiades satellite imagery with airborne LiDAR data , 2020 .
[49] Dar A. Roberts,et al. Mapping urban forest structure and function using hyperspectral imagery and lidar data , 2016 .
[50] Ning Han,et al. Exploring the synergistic use of multi-scale image object metrics for land-use/land-cover mapping using an object-based approach , 2015 .
[51] Omid Abdi,et al. Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis , 2019, Sensors.
[52] D. Roberts,et al. Urban tree species mapping using hyperspectral and lidar data fusion , 2014 .
[53] Dan Zhang,et al. Spatial estimation of urban forest structures with Landsat TM data and field measurements , 2015 .
[54] Gui-Song Xia,et al. Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery , 2015, Remote. Sens..
[55] Zhenzhong Chen,et al. Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation , 2020 .
[56] William J. Emery,et al. Contextually guided very-high-resolution imagery classification with semantic segments , 2017 .
[57] Iryna Dronova,et al. Spectral vegetation indices of wetland greenness: Responses to vegetation structure, composition, and spatial distribution , 2019 .