Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth's
land surface within the scope of Earth observation (EO). Technological advances in capture …

Very high resolution (VHR) remote sensing (RS) scene classification is a challenging task
due to the higher inter-class similarity and intra-class variability problems. Recently, the …

With the rapid growth of aerial image quantity and quality, the performance of aerial scene
classifiers based on deep learning models has also achieved tremendous success …

Convolutional neural networks (CNNs), in particular, demonstrate the remarkable power of
feature learning in remote sensing for land use and cover classification, as demonstrated by …

Multilabel classification in remote sensing is very significant and plays an important role in
extracting valuable information from satellite imagery. Ignoring the distinct information …

Data quality plays a vital role in scientific research and decision-making across industries.
Thus, it is crucial to incorporate the data quality control (DQC) process, which comprises …

Remote sensing is a long-distance measuring technology that obtains data about a
phenomenon or an object. Remote sensing technology plays a crucial role in several …

Scene classification for remote sensing image (RSI) of land use and land cover (LULC)
involves identifying discriminative features of interest in different classes. Spurred by the …

Land-use scene classification (LUSC) is a key technique in the field of remote sensing
imagery (RSI) interpretation. A convolutional neural network (CNN) is widely used for its …

Purpose Classification of remote sensing images (RSI) is a challenging task in computer
vision. Recently, researchers have proposed a variety of creative methods for automatic …