The Potential of Deep Features for Small Object Class Identification in Very High Resolution Remote Sensing Imagery

2017 ◽  
pp. 569-577
Author(s):  
M. Dahmane ◽  
S. Foucher ◽  
M. Beaulieu ◽  
Y. Bouroubi ◽  
M. Benoit
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Small Object ◽  
Object Class ◽  
Remote Sensing Imagery ◽  
Class Identification ◽  
Very High

scholarly journals A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery

2016 ◽  
Vol 8 (10) ◽  
pp. 814 ◽  
Author(s):  
ZhiYong Lv ◽  
Haiqing He ◽  
Jón Benediktsson ◽  
Hong Huang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Supervised Classification ◽  
Remote Sensing Imagery ◽  
Very High

scholarly journals Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

2018 ◽  
Vol 10 (1) ◽  
pp. 144 ◽  
Author(s):  
Yongyang Xu ◽  
Liang Wu ◽  
Zhong Xie ◽  
Zhanlong Chen
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Building Extraction ◽  
Remote Sensing Imagery ◽  
Very High

scholarly journals Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model

2020 ◽  
Vol 12 (15) ◽  
pp. 2426
Author(s):  
Alin-Ionuț Pleșoianu ◽  
Mihai-Sorin Stupariu ◽  
Ionuț Șandric ◽  
Ileana Pătru-Stupariu ◽  
Lucian Drăguț
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Ensemble Model ◽  
Tree Crown ◽  
Species Classification ◽  
Individual Tree ◽  
Ensemble Models ◽  
Remote Sensing Imagery ◽  
Very High

Traditional methods for individual tree-crown (ITC) detection (image classification, segmentation, template matching, etc.) applied to very high-resolution remote sensing imagery have been shown to struggle in disparate landscape types or image resolutions due to scale problems and information complexity. Deep learning promised to overcome these shortcomings due to its superior performance and versatility, proven with reported detection rates of ~90%. However, such models still find their limits in transferability across study areas, because of different tree conditions (e.g., isolated trees vs. compact forests) and/or resolutions of the input data. This study introduces a highly replicable deep learning ensemble design for ITC detection and species classification based on the established single shot detector (SSD) model. The ensemble model design is based on varying the input data for the SSD models, coupled with a voting strategy for the output predictions. Very high-resolution unmanned aerial vehicles (UAV), aerial remote sensing imagery and elevation data are used in different combinations to test the performance of the ensemble models in three study sites with highly contrasting spatial patterns. The results show that ensemble models perform better than any single SSD model, regardless of the local tree conditions or image resolution. The detection performance and the accuracy rates improved by 3–18% with only as few as two participant single models, regardless of the study site. However, when more than two models were included, the performance of the ensemble models only improved slightly and even dropped.

Sign in / Sign up

Export Citation Format

Share Document