MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing

By Valerio Marsocci, Simone Scardapane, Nikos Komodakis. 2021

31 citations recorded by the source corpus.

Scene understanding of satellite and aerial images is a pivotal task in various remote sensing (RS) practices, such as land cover and urban development monitoring. In recent years, neural networks have become a de-facto standard in many of these applications. However, semantic segmentation still remains a challenging task. With respect to other computer vision (CV) areas, in RS large labeled datasets are not very often available, due to their large cost and to the required manpower. On the other hand, self-supervised learning (SSL) is earning more and more interest in CV, reaching state-of-the-art in several tasks. In spite of this, most SSL models, pretrained on huge datasets like ImageNet, do not perform particularly well on RS data. For this reason, we propose a combination of a SSL algorithm (particularly, Online Bag of Words) and a semantic segmentation algorithm, shaped for aerial images (namely, Multistage Attention ResU-Net), to show new encouraging results (i.e., 81.76% mIoU with ResNet-18 backbone) on the ISPRS Vaihingen dataset.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Valerio Marsocci
2. Simone Scardapane
3. Laura Palagi

In Computer Vision and Pattern Recognition

1. Valerio Marsocci
2. Simone Scardapane
3. Laura Palagi

In Media Technology

1. Simone Scardapane
2. Valerio Marsocci

Citations in the corpus

• Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation (2022, methodology): “In [13], the authors perform a semantic segmentation downstream task on the Vaihingen”