Object Classification in Remote Sensing Imagery holds paramount importance for extracting
meaningful insights from complex aerial scenes. Conventional methods encounter …

Recently, ensemble multiple deep learning (DL) classifiers has been reported to be an
effective method for improving remote sensing classification accuracy. Although these …

Recently, time series image (TSI) has been reported to be an effective resource to mapping
fine land use/land cover (LULC), and deep learning, in particular, has been gaining growing …

Remote sensing monitoring of glacial lakes is an indispensable tool for identifying and
preventing glacial lake disasters. At present, the existing extraction methods of glacial lakes …

Recent advances in airborne and space-based remote sensing technologies and a rapid
increase in the use of machine learning (ML) techniques in digital image processing …

Recently, deep learning algorithms, specifically convolutional neural networks (CNNs), have
played an important role in remote sensing image classification, including wetland mapping …

Remote sensing images (RSI) are significant data to examine and observe complete
structure on the Earth's surface. RSI classification has gained significant attention in earth …

Accurate and efficient landslide identification is an important basis for landslide disaster
prevention and control. Due to the diversity of landslide features, vegetation occlusion, and …

Exploiting machine learning techniques to automatically classify multispectral remote
sensing imagery plays a significant role in deriving changes on the Earth's surface …

Aiming at the problems of low classification accuracy and time‐consuming properties in
traditional remote sensing image classification methods, a remote sensing image …