Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …

Remote sensing image scene classification is an active and challenging task driven by
many applications. More recently, with the advances of deep learning models especially …

Accurate and robust detection of multi-class objects in optical remote sensing images is
essential to many real-world applications, such as urban planning, traffic control, searching …

Benchmark datasets are critical for developing, evaluating, and comparing remote sensing
image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in …

As one of the most challenging tasks of remote sensing big data mining, large-scale remote
sensing image retrieval has attracted increasing attention from researchers. Existing large …

Fine-tuning of pretrained convolutional neural networks (CNNs) has been proven to be an
effective strategy for remote sensing image scene classification, particularly when a limited …

Hyperspectral image (HSI) classification aims to distinguish the category of a land coverage
object for each pixel. In an effective way, the transformer architecture has been successfully …

Scene classification is a fundamental task for automatic remote sensing image
understanding. In recent years, convolutional neural networks have become a hot research …

High spatial resolution (HSR) imagery scene classification, which involves labeling an HSR
image with a specific semantic class according to the geographical properties, has received …

Recently, many state-of-the-art results for remote sensing image scene classification have
been achieved by convolutional neural networks (CNNs) due to their large learning …