Results 211 to 220 of about 49,901 (265)

MAR-YOLO: multi-scale feature adaptive selection and asymptotic pyramid for oriented Building detection in remote sensing images. [PDF]

Sci Rep
Zhao Y, Qian H.
europepmc   +1 more source

A multi-branch feature enhancement-based detection and hierarchical chaotic encryption fusion method for sensitive targets in remote sensing images. [PDF]

Sci Rep
Zhang Q, Wang H, Li X, Zhang S, Liu J.
europepmc   +1 more source

RETRACTION: A Method for Extracting Building Information from Remote Sensing Images Based on Deep Learning. [PDF]

Comput Intell Neurosci
Neuroscience CIA.
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RSW-YOLO: A Vehicle Detection Model for Urban UAV Remote Sensing Images. [PDF]

Sensors (Basel)
Wang H, Shang J, Wang X, Zhang Q, Wang X, Li J, Wang Y.   +6 more
europepmc   +1 more source

Detection of Pine Wilt Disease in UAV Remote Sensing Images Based on SLMW-Net. [PDF]

Plants (Basel)
Yuan X, Zhou G, Yan Y, Yan X.
europepmc   +1 more source

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Remote sensing image synthesis

2010 IEEE International Geoscience and Remote Sensing Symposium, 2010
For remote sensing data, the testing analysis tools is difficult since the ground-truth data are not available in many cases. To address this issue, a novel method for image synthesis is presented for use as a evaluation test-bed. Given the scale-dependent, non-stationary nature of remotely sensed data, a new modeling approach that combines a ...
Ying Liu 0010, Alexander Wong, Paul W. Fieguth   +2 more
openaire   +1 more source

Remote Sensing Image Compression: A Review

2015 IEEE International Conference on Multimedia Big Data, 2015
With the increasing spatial and temporal resolutions of acquired remote sensing (RS) images, effective image compression is becoming more and more important. RS image compression technologies have been extensively studied in the past a few decades, and various algorithms have been developed accordingly.
Shichao Zhou, Chenwei Deng, Baojun Zhao, Yatong Xia, Qisheng Li, Zhenzhong Chen 0001   +5 more
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Single Remote Sensing Image Dehazing

IEEE Geoscience and Remote Sensing Letters, 2014
Remote sensing images are widely used in various fields. However, they usually suffer from the poor contrast caused by haze. In this letter, we propose a simple, but effective, way to eliminate the haze effect on remote sensing images. Our work is based on the dark channel prior and a common haze imaging model.
Jiao Long, Zhenwei Shi 0001, Wei Tang 0016, Changshui Zhang   +3 more
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Retrieving Images for Remote Sensing Applications

2006
A unique way in which content based image retrieval (CBIR) for remote sensing differs widely from traditional CBIR is the widespread occurrences of weak textures. The task of representing the weak textures becomes even more challenging especially if image properties like scale, illumination or the viewing geometry are not known.
SAWANT, N, CHANDRAN, S, MOHAN, BK
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