In remote sensing (RS), use of single optical sensors is frequently inadequate for practical
Earth observation applications (eg, agricultural, forest, ecology monitoring) due to trade-offs …

Change detection is an essential and widely utilized task in remote sensing that aims to
detect and analyze changes occurring in the same geographical area over time, which has …

Change detection (CD) of remote sensing (RS) images is one of the important problems in
earth observation, which has been extensively studied in recent years. However, with the …

Change detection on multimodal remote sensing images has become an increasingly
interesting and challenging topic in the remote sensing community, which can play an …

Unsupervised multimodal change detection is a practical and challenging topic that can play
an important role in time-sensitive emergency applications. To address the challenge that …

This work investigates unsupervised domain adaptation (UDA)-based semantic
segmentation of very high-resolution (VHR) remote sensing (RS) images from different …

Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

An increasing availability of remote sensing data in the era of geo big-data makes producing
well-represented, reliable training data to be more challenging and requires an excessive …

Change detection (CD) by comparing two bitemporal images is a crucial task in remote
sensing. With the advantages of requiring no cumbersome labeled change information …

Due to the complementary properties of different types of sensors, change detection
between heterogeneous images receives increasing attention from researchers. However …