4 papers
A Hierarchical Self-Consistent Regularization Approach to Satellite Image Time Series Classification
Giulio Weikmann, Gianmarco Perantoni, Lorenzo Bruzzone
Deep learning has become increasingly important in remote sensing image classification due to its ability to extract semantic information from complex data. Classification tasks of…
Adaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classification
Chenying Liu, Gianmarco Perantoni, Lorenzo Bruzzone +1
Multi-label classification (MLC) offers a more comprehensive semantic understanding of Remote Sensing (RS) imagery compared to traditional single-label classification (SLC). Howeve…
A class-driven hierarchical ResNet for classification of multispectral remote sensing images
Giulio Weikmann, Gianmarco Perantoni, Lorenzo Bruzzone
This work presents a multitemporal class-driven hierarchical Residual Neural Network (ResNet) designed for modelling the classification of Time Series (TS) of multispectral images…
A deep multiple instance learning approach based on coarse labels for high-resolution land-cover mapping
Gianmarco Perantoni, Lorenzo Bruzzone
The quantity and the quality of the training labels are central problems in high-resolution land-cover mapping with machine-learning-based solutions. In this context, weak labels c…