Results 281 to 290 of about 300,366 (322)

Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions

IEEE Geoscience and Remote Sensing Magazine, 2021
Change detection is a vibrant area of research in remote sensing. Thanks to increases in the spatial resolution of remote sensing images, subtle changes at a finer geometrical scale can now be effectively detected.
Dawei Wen, Xin Huang, F. Bovolo, Jiayi Li, X. Ke, A. Zhang, J. Benediktsson   +6 more
semanticscholar   +1 more source

TransUNetCD: A Hybrid Transformer Network for Change Detection in Optical Remote-Sensing Images

IEEE Transactions on Geoscience and Remote Sensing, 2022
In the change detection (CD) task, the UNet architecture has achieved superior results. However, due to the inherent limitation of convolution operations, UNet is inadequate in learning global context and long-range spatial relations.
Qingyang Li, Ruofei Zhong, Xin Du, Yuying Du   +3 more
semanticscholar   +1 more source

Detection of Multiclass Objects in Optical Remote Sensing Images

IEEE Geoscience and Remote Sensing Letters, 2019
Object detection in complex optical remote sensing images is a challenging problem due to the wide variety of scales, densities, and shapes of object instances on the earth surface. In this letter, we focus on the wide-scale variation problem of multiclass object detection and propose an effective object detection framework in remote sensing images ...
Wenchao Liu, Long Ma, Jue Wang, He Chen
openaire   +1 more source

Ship Detection in High-Resolution Optical Remote Sensing Images Aided by Saliency Information

IEEE Transactions on Geoscience and Remote Sensing, 2022
Ship detection is a crucial but challenging task in optical remote sensing images. Recently, thanks to the emergence of deep neural networks, significant progress has been made in ship detection.
Zhida Ren, Yongqiang Tang, Zewen He, L. Tian, Yang Yang, Wensheng Zhang   +5 more
semanticscholar   +1 more source

ASNet: Adaptive Semantic Network Based on Transformer–CNN for Salient Object Detection in Optical Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing
Salient object detection in optical remote sensing images (RSI-SOD) has recently become a key area of research, driven by the unique challenges posed by specific imaging conditions.
Ruixiang Yan, Long-He Yan, Guohua Geng, Yufei Cao, Pengbo Zhou, Yongle Meng   +5 more
semanticscholar   +1 more source

Bayesian Vehicle Detection Using Optical Remote Sensing Images

2018
Automatic object detection is a widely investigated problem in different fields such as military and urban surveillance. The availability of Very High Resolution (VHR) optical remotely sensed data, has motivated the design of new object detection methods that allow recognizing small objects like ships, buildings and vehicles.
Walma Gharbi, Lotfi Chaari, Amel Benazza-Benyahia   +2 more
openaire   +1 more source

Fully Squeezed Multiscale Inference Network for Fast and Accurate Saliency Detection in Optical Remote-Sensing Images

IEEE Geoscience and Remote Sensing Letters, 2022
Recently, salient object detection in optical remote-sensing images (RSIs) has received more and more attention. To tackle the challenges of RSIs including large-scale variation of objects, cluttered background, irregular shape of objects, and big ...
Kunye Shen, Xiaofei Zhou, Bin Wan, Ran Shi, Jiyong Zhang   +4 more
semanticscholar   +1 more source

Cloud Detection in Optical Remote Sensing Images With Deep Semi-Supervised and Active Learning

IEEE Geoscience and Remote Sensing Letters, 2023
Clouds hinder the surface observation by optical remote sensing sensors. It is of great significance to detect clouds and nonclouds in remote sensing images.
Xudong Yao, Qing Guo, An Li
semanticscholar   +1 more source

Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey

Remote Sensing
In optical remote sensing images, the presence of clouds affects the completeness of the ground observation and further affects the accuracy and efficiency of remote sensing applications.
Zhengxin Wang, Longlong Zhao, Jintao Meng, Yu Han, Xiaoli Li, Ruixia Jiang, Jinsong Chen, Hongzhong Li   +7 more
semanticscholar   +1 more source

MeSAM: Multiscale Enhanced Segment Anything Model for Optical Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing
Segment anything model (SAM) has been widely applied to various downstream tasks for its excellent performance and generalization capability. However, SAM exhibits three limitations related to remote sensing (RS) semantic segmentation task: 1) the image ...
Xichuan Zhou, Fu Liang, Lihui Chen, Haijun Liu, Qianqian Song, G. Vivone, Jocelyn Chanussot   +6 more
semanticscholar   +1 more source