Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks

[1]  D. Leckie,et al.  The Individual Tree Crown Approach Applied to Ikonos Images of a Coniferous Plantation Area , 2006 .

[2]  Weijia Li,et al.  Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images , 2016, Remote. Sens..

[3]  Hao Wu,et al.  Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification , 2018, IEEE Transactions on Image Processing.

[4]  Christopher B Anderson,et al.  Biodiversity monitoring, earth observations and the ecology of scale. , 2018, Ecology letters.

[5]  Jungho Im,et al.  A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification , 2015 .

[6]  Michele Dalponte,et al.  Tree‐centric mapping of forest carbon density from airborne laser scanning and hyperspectral data , 2016, Methods in ecology and evolution.

[7]  Juha Hyyppä,et al.  Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms , 2016, Remote. Sens..

[8]  Michael A. Wulder,et al.  Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities , 2006 .

[9]  Arko Lucieer,et al.  Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Ben. G. Weinstein A computer vision for animal ecology. , 2018, The Journal of animal ecology.

[11]  Daniel J. Hayes,et al.  The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory , 2018, Remote. Sens..

[12]  Michael Weinmann,et al.  A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas , 2017, Remote. Sens..

[13]  Laura S. Kenefic,et al.  Layer Stacking: A Novel Algorithm for Individual Forest Tree Segmentation from LiDAR Point Clouds , 2017 .

[14]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  George C. Hurtt,et al.  An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems , 2014 .

[16]  Juha Hyyppä,et al.  Combination of individual tree detection and area-based approach in imputation of forest variables using airborne laser data , 2012 .

[17]  Maggi Kelly,et al.  A New Method for Segmenting Individual Trees from the Lidar Point Cloud , 2012 .

[18]  Jerry F Franklin,et al.  Applying LiDAR Individual Tree Detection to Management of Structurally Diverse Forest Landscapes , 2018, Journal of Forestry.

[19]  Michele Dalponte,et al.  Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data , 2017 .

[20]  Sarah J. Graves,et al.  A hyperspectral image can predict tropical tree growth rates in single-species stands. , 2016, Ecological applications : a publication of the Ecological Society of America.

[21]  Huawu Deng,et al.  Individual tree crown detection in sub-meter satellite imagery using Marked Point Processes and a geometrical-optical model , 2018, Remote Sensing of Environment.

[22]  C. Silva,et al.  Imputation of Individual Longleaf Pine (Pinus palustris Mill.) Tree Attributes from Field and LiDAR Data , 2016 .

[23]  Bin Wu,et al.  Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Le Wang,et al.  Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges , 2019, Remote Sensing of Environment.

[25]  Warren B. Cohen,et al.  Mapping post-fire habitat characteristics through the fusion of remote sensing tools , 2014 .

[26]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[27]  Francisco Herrera,et al.  Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study , 2017, Remote. Sens..