A Contrastive Continual Learning for the Classification of Remote Sensing Imagery

By Abdulaziz S. Alakooz, Nassim Ammour. 2022

5 citations recorded by the source corpus.

Since the emergence of computer science, the researching community has been pushing the boundaries to produce an artificially intelligent machines. One of the most sought-after goals is to mimic many of human abilities like incremental learning. In our case, we intend to perform remote sensing (RS) classification task, which takes images of earth/land captured by unmanned aerial vehicles (UAVs) and classifies them to a specific land type/use. In this paper, we propose a contrastive learning-based deep neural network model to classify remote sensing images in an incremental learning environment. The deep learning model is continually confronted with new tasks and data and has to perform well without seriously suffering from catastrophic forgetting. Experiments are conducted on well-known RS dataset: UC-Merced. The experiments results show substantial improvement of overall accuracy using contrastive losses compared to traditional Cross-Entropy loss in an incremental learning scenario.

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, background): “The authors in [32] and [33] make use of contrastive learning to learn effective representations that can reduce catastrophic”