With rapid development of satellite and airplane platforms, aerial image has become more
and more accessible. Aerial image scene classification plays an important role in many …

Deep learning methods have boosted the performance of a series of visual tasks. However,
the aerial image scene classification remains challenging. The object distribution and spatial …

Compared with natural scenes, aerial scenes are usually composed of numerous objects
densely distributed within the aerial view, and thus, more key local semantic features are …

As a fundamental problem in earth observation, aerial scene classification tries to assign a
specific semantic label to an aerial image. In recent years, the deep convolutional neural …

The classification performance of aerial scenes relies heavily on the discriminative power of
feature representation from high-spatial resolution remotely sensed imagery. The …

Aerial image scene classification is a fundamental problem for understanding high-
resolution remote sensing images and has become an active research task in the field of …

With the development of deep learning, supervised feature learning methods have achieved
prominent performance in the field of aerial scene classification. However, supervised …

Aerial scene recognition is challenging due to the complicated object distribution and spatial
arrangement in a large-scale aerial image. Recent studies attempt to explore the local …

Remote sensing image scene classification plays an important role in remote sensing image
interpretation. Deep learning brings prosperity to the research in this field, and numerous …

Remote sensing image scene classification has been widely applied and has attracted
increasing attention. Recently, convolutional neural networks (CNNs) have achieved …