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Segformer B5 Remote Sensing Quality
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Segformer B5 Remote Sensing Quality
Developed by yuyijiong
A semantic segmentation model for remote sensing images based on SegFormer architecture, designed to detect 6 types of image quality defects
Downloads 15
Release Time : 4/20/2024
Model Overview
This model employs semantic segmentation technology to inspect the quality of remote sensing images, capable of identifying 6 common quality issues such as cloud occlusion and shadowed areas. It is suitable for remote sensing image preprocessing and quality control scenarios.
Model Features
Multi-defect Type Detection
Capable of simultaneously identifying 6 common quality issues in remote sensing images, including cloud occlusion and shadowed areas
Efficient Segmentation Architecture
Lightweight design based on SegFormer, improving inference efficiency while maintaining accuracy
Remote Sensing Scenario Optimization
Specially optimized for remote sensing image characteristics, adapting to large dimensions and multi-spectral features
Model Capabilities
Remote Sensing Image Analysis
Quality Defect Detection
Pixel-level Semantic Segmentation
Multi-category Anomaly Recognition
Use Cases
Remote Sensing Data Processing
Satellite Image Quality Control
Automatically detects quality issues during satellite image reception
Identifies and labels regions with 6 types of quality defects
Geographic Information System Preprocessing
Provides GIS systems with quality-qualified input images
Filters out images with severe quality issues
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