EARTHQUAKE-DAMAGED REGIONS DETECTION FROM HIGH RESOLUTION IMAGE BASED ON SUPER-PIXEL SEGMENTATION AND DEEP LEARNING
Accurate detection and automatic processing of earthquake-damaged regions is essential for effective rescue and post-disaster reconstruction. In this study, we proposed a Combined Super-pixel Segmentation and AlexNet Detection approach (CSSAD) for automatically extracting damaged regions from post-e...
Main Authors: | C. Liu, H. Sui, Y. Peng, L. Hua, Q. Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2020-08-01
|
Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/45/2020/isprs-annals-V-3-2020-45-2020.pdf |
Similar Items
-
Super Resolution Imaging Based on a Dynamic Single Pixel Camera
by: Yao Zhao, et al.
Published: (2017-01-01) -
Segmentation of natural images based on super pixel and graph merging
by: Aritra Mukherjee, et al.
Published: (2021-02-01) -
Sub-pixel Layout for Super-Resolution with Images in the Octic Group
by: Shi, Boxin, et al.
Published: (2015) -
Blur Image Segmentation using Iterative Super Pixels Grouping Method
by: Kuan-Lin Yu, et al.
Published: (2012) -
Towards pixel-to-pixel deep nucleus detection in microscopy images
by: Fuyong Xing, et al.
Published: (2019-09-01)