Performance and Analysis of FCN, U-Net, and SegNet in Remote Sensing Image Segmentation Based on the LoveDA Dataset
Remote sensing image segmentation is a vital method in image analysis that significantly contributes to the extraction of surface information and aids in land use planning. This study utilizes the LoveDA dataset to investigate the segmentation performance of three classic deep learning models: Fully...
Saved in:
Main Author: | Yang Shuhao |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2025-01-01
|
Series: | ITM Web of Conferences |
Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03023.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
RenalSegNet: automated segmentation of renal tumor, veins, and arteries in contrast-enhanced CT scans
by: Rashid Khan, et al.
Published: (2025-01-01) -
STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing Images
by: Wei Wu, et al.
Published: (2025-01-01) -
Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation
by: Jin Zixiao
Published: (2025-01-01) -
Detection of Masses in Mammogram Images Based on the Enhanced RetinaNet Network With INbreast Dataset
by: Wang M, et al.
Published: (2025-02-01) -
MitoSeg: Mitochondria segmentation tool
by: Faris Serdar Taşel, et al.
Published: (2025-05-01)