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 …

Substantial efforts have been devoted more recently to presenting various methods for
object detection in optical remote sensing images. However, the current survey of datasets …

Oriented object detection is a practical and challenging task in remote sensing image
interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to …

Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …

Hyperspectral image (HSI) classification refers to identifying land-cover categories of pixels
based on spectral signatures and spatial information of HSIs. In recent deep learning-based …

Surface defect detection of products is an important process to guarantee the quality of
industrial production. A defect detection task aims to identify the specific category and …

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …

Benefiting from the development of convolutional neural networks (CNNs), many excellent
algorithms for object detection have been presented. Remote sensing object detection …

With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

Object detection has been a building block in computer vision. Though considerable
progress has been made, there still exist challenges for objects with small size, arbitrary …