SaNet: Scale-aware Neural Network for Semantic Labelling of Multiple Spatial Resolution Aerial Images

Assigning geospatial objects of aerial images with specific categories at the pixel level is a fundamental task in urban scene interpretation. Along with rapid developments in sensor technologies, aerial images can be captured at multiple spatial resolutions (MSR) with information content manifested at different scales... (read more)

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Methods used in the Paper


METHOD TYPE
ReLU
Activation Functions
Batch Normalization
Normalization
Average Pooling
Pooling Operations
Pyramid Pooling Module
Semantic Segmentation Modules
1x1 Convolution
Convolutions
Auxiliary Classifier
Miscellaneous Components
Convolution
Convolutions
Dilated Convolution
Convolutions
PSPNet
Semantic Segmentation Models
FPN
Feature Extractors
SANet
Image Models