TUP2.PD.6
DNN-Based Cladonia Lichen Mapping Using AVIRIS-NG Hyperspectral Imagery and UAV Images in a Rocky Canadian Shield Landscape
Shahab Jozdani, Dongmei Chen, Queen's University, Canada; Wenjun Chen, Sylvain G. Leblanc, Canada Centre for Remote Sensing, Canada
Session:
TUP2.PD: Remote Detection of Invasive Weed Species II Poster
Track:
Community-Contributed Sessions
Location:
Poster Area
Presentation Time:
Tuesday, 5 August, 14:30 - 15:45
Session Co-Chairs:
Kathryn Sheffield, Department of Energy, Environment and Climate Action and Deepak Gautam, RMIT University
Presentation
Discussion
Resources
No resources available.
Session TUP2.PD
TUP2.PD.1: Classifying Serrated Tussock Cover from Aerial Imagery Using RGB Bands, RGB Indices, and Texture Features
Daniel Pham, RMIT University, Australia
TUP2.PD.2: TOWARDS COST EFFECTIVE AUTONOMOUS MAPPING OF INVASIVE AQUATIC PLANTS USING DEEP LEARNING AND A GPU ENABLED MICROCOMPUTER
Swarup Bhattarai, Mississippi State University, United States; Sathishkumar Samiappan, University of Tennessee at Knoxville, United States; Piyush Chaudhary, Daniel McCraine, Gray Turnage, Mississippi State University, United States
TUP2.PD.3: MAPPING AND CLASSIFICATION OF INVASIVE TREE SPECIES CHINESE CELTIS IN RIPARIAN ECOSYSTEMS
Aranya Jha, University of Twente, Netherlands; Armando Apan, Bikram Banerjee, University of Sothern Queensland, Australia
TUP2.PD.4: Remote sensing classification of vegetation with deep learning model embedded in a mountain vegetation knowledge graph ontology
Yonghui Yao, Jun Xu, Ruixiang Shi, 中国科学院地理科学与资源研究所, China
TUP2.PD.5: MAPPING WETLANDS IN SWEDEN USING MULTI-SOURCE SATELLITE DATA AND RANDOM FOREST ALGORITHM
Salvador Hernández Malavé, Zheng Duan, Lund University, Sweden
TUP2.PD.6: DNN-Based Cladonia Lichen Mapping Using AVIRIS-NG Hyperspectral Imagery and UAV Images in a Rocky Canadian Shield Landscape
Shahab Jozdani, Dongmei Chen, Queen's University, Canada; Wenjun Chen, Sylvain G. Leblanc, Canada Centre for Remote Sensing, Canada
Resources
No resources available.