Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network
Object detection on very-high-resolution (VHR) remote sensing imagery has attracted a lot of attention in the field of image automatic interpretation. Region-based convolutional neural networks (CNNs) have been vastly promoted in this domain, which first generate candidate regions and then accuratel...
Main Authors: | Xiaodong Zhang, Kun Zhu, Guanzhou Chen, Xiaoliang Tan, Lifei Zhang, Fan Dai, Puyun Liao, Yuanfu Gong |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-03-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/11/7/755 |
Similar Items
-
The Potential of Satellite Imagery for Surveying Whales
by: Caroline Höschle, et al.
Published: (2021-02-01) -
Novel Multi-Scale Filter Profile-Based Framework for VHR Remote Sensing Image Classification
by: Zhiyong Lv, et al.
Published: (2019-09-01) -
Engaging ‘the crowd’ in remote sensing to learn about habitat affinity of the Weddell seal in Antarctica
by: Michelle A. LaRue, et al.
Published: (2020-03-01) -
Remote Sensing Imagery Super Resolution Based on Adaptive Multi-Scale Feature Fusion Network
by: Xinying Wang, et al.
Published: (2020-02-01) -
Multi-Channel Feature Pyramid Networks for Prostate Segmentation, Based on Transrectal Ultrasound Imaging
by: Lei Geng, et al.
Published: (2020-05-01)