Remote sensing object detection meets deep learning: A metareview of challenges and advances
Remote sensing object detection (RSOD), one of the most fundamental and challenging
tasks in the remote sensing field, has received long-standing attention. In recent years, deep …
tasks in the remote sensing field, has received long-standing attention. In recent years, deep …
Learning to aggregate multi-scale context for instance segmentation in remote sensing images
The task of instance segmentation in remote sensing images, aiming at performing per-pixel
labeling of objects at the instance level, is of great importance for various civil applications …
labeling of objects at the instance level, is of great importance for various civil applications …
Localizing from classification: Self-directed weakly supervised object localization for remote sensing images
In recent years, object localization and detection methods in remote sensing images (RSIs)
have received increasing attention due to their broad applications. However, most previous …
have received increasing attention due to their broad applications. However, most previous …
[HTML][HTML] Self-training guided disentangled adaptation for cross-domain remote sensing image semantic segmentation
Remote sensing (RS) image semantic segmentation using deep convolutional neural
networks (DCNNs) has shown great success in various applications. However, the high …
networks (DCNNs) has shown great success in various applications. However, the high …
Cluster-memory augmented deep autoencoder via optimal transportation for hyperspectral anomaly detection
Hyperspectral anomaly detection (AD) aims to detect objects significantly different from their
surrounding background. Recently, many detectors based on autoencoder (AE) exhibited …
surrounding background. Recently, many detectors based on autoencoder (AE) exhibited …
Learning oriented object detection via naive geometric computing
Y Wang, Z Zhang, W Xu, L Chen… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
Detecting oriented objects along with estimating their rotation information is one crucial step
for image analysis, especially for remote sensing images. Despite that many methods …
for image analysis, especially for remote sensing images. Despite that many methods …
Efficient pyramidal GAN for versatile missing data reconstruction in remote sensing images
Missing data reconstruction is a classical, yet challenging problem in remote sensing (RS)
image processing due to the complex atmospheric environment and variability of satellite …
image processing due to the complex atmospheric environment and variability of satellite …
A multi-scale progressive collaborative attention network for remote sensing fusion classification
With the development of remote sensing technology, panchromatic images (PANs) and
multispectral images (MSs) can be easily obtained. PAN has higher spatial resolution, while …
multispectral images (MSs) can be easily obtained. PAN has higher spatial resolution, while …
An adaptive migration collaborative network for multimodal image classification
The multispectral (MS) and the panchromatic (PAN) images belong to different modalities
with specific advantageous properties. Therefore, there is a large representation gap …
with specific advantageous properties. Therefore, there is a large representation gap …
Aerial images meet crowdsourced trajectories: a new approach to robust road extraction
Land remote-sensing analysis is a crucial research in earth science. In this work, we focus
on a challenging task of land analysis, ie, automatic extraction of traffic roads from remote …
on a challenging task of land analysis, ie, automatic extraction of traffic roads from remote …