6 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…
Bayesian Modelling of Multi-Year Crop Type Classification Using Deep Neural Networks and Hidden Markov Models
Gianmarco Perantoni, Giulio Weikmann, Lorenzo Bruzzone
The temporal consistency of yearly land-cover maps is of great importance to model the evolution and change of the land cover over the years. In this paper, we focus the attention…
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…
A Novel Technique for Robust Training of Deep Networks With Multisource Weak Labeled Remote Sensing Data
Gianmarco Perantoni, Lorenzo Bruzzone
Deep learning has gained broad interest in remote sensing image scene classification thanks to the effectiveness of deep neural networks in extracting the semantics from complex da…