In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …

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 …

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 …

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

In he past decade, object detection has achieved significant progress in natural images but
not in aerial images, due to the massive variations in the scale and orientation of objects …

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 …

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 …

Deep learning-based object detection and instance segmentation have achieved
unprecedented progress. In this article, we propose complete-IoU (CIoU) loss and Cluster …

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 …

Multi-level feature fusion is a fundamental topic in computer vision for detecting, segmenting
and classifying objects at various scales. When multi-level features meet multi-modal cues …