Coconut trees detection and segmentation in aerial imagery using mask region‐based convolution neural network
Abstract Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis. Under such scenarios, speedy assessment of food resources from agricultural land is critical as it supports aid activity in the disaster‐hit areas. In this article,...
Main Authors: | Muhammad Shakaib Iqbal, Hazrat Ali, Son N. Tran, Talha Iqbal |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-09-01
|
Series: | IET Computer Vision |
Online Access: | https://doi.org/10.1049/cvi2.12028 |
Similar Items
-
SEGMENT-AND-COUNT: VEHICLE COUNTING IN AERIAL IMAGERY USING ATROUS CONVOLUTIONAL NEURAL NETWORKS
by: S. Azimi, et al.
Published: (2018-09-01) -
Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks
by: Ovidiu Csillik, et al.
Published: (2018-11-01) -
SEMANTIC SEGMENTATION OF AERIAL IMAGERY VIA MULTI-SCALE SHUFFLING CONVOLUTIONAL NEURAL NETWORKS WITH DEEP SUPERVISION
by: K. Chen, et al.
Published: (2018-09-01) -
SINGLE-IMAGE DEHAZING ON AERIAL IMAGERY USING CONVOLUTIONAL NEURAL NETWORKS
by: M. Madadikhaljan, et al.
Published: (2019-10-01) -
Automatic Building Segmentation of Aerial Imagery Using Multi-Constraint Fully Convolutional Networks
by: Guangming Wu, et al.
Published: (2018-03-01)