This paper unveils the discoveries and outcomes of the inaugural iteration of the Multi-modal
Aerial View Image Challenge (MAVIC) aimed at image translation. The primary objective of …

The detection and recognition of ships holds crucial significance across various domains,
including transportation, fishing, smuggling prevention, and rescue operations. To achieve …

Automatic target recognition (ATR) using image data is an important computer vision task
with widespread applications in remote sensing for surveillance, object tracking, urban …

Multi-view learning (MVL) is a promising field that seeks to make the most of information
shared across different views, ultimately improving generalization performance. Numerous …

Multimodal fusion networks play a pivotal role in leveraging diverse sources of information
for enhanced machine learning applications in aerial imagery. However, current approaches …

Fusion of multimodal data can offer enhanced machine learning. One of the most common
fusion approaches in deep learning is end-to-end training of a neural network on all …

This research paper introduces an innovative method for land cover classification in satellite
imagery, specifically designed to address the challenges posed by mixed pixels and edge …

Many techniques have been developed for sensor and information fusion, machine and
deep learning, as well as data and machine analytics. Currently, many groups are exploring …

Multi-modal aerial view object classification (MAVOC) in Automatic target recognition (ATR),
although an important and challenging problem, has been under studied. This paper firstly …

This paper considers the problem of aerial view object classification using co-registered
electro-optical (EO) and synthetic aperture radar (SAR) images. Both EO and SAR sensors …