Results 211 to 220 of about 46,904 (268)

Time Series Remote Sensing Image Classification with a Data-Driven Active Deep Learning Approach. [PDF]

Sensors (Basel)
Xie G, Liu P, Chen Z, Chen L, Ma Y, Zhao L.   +5 more
europepmc   +1 more source

A Method Combining Discrete Cosine Transform with Attention for Multi-Temporal Remote Sensing Image Matching. [PDF]

Sensors (Basel)
Zeng Q, Hui B, Liu Z, Xu Z, He M.
europepmc   +1 more source

A new band selection framework for hyperspectral remote sensing image classification. [PDF]

Sci Rep
Phaneendra Kumar BLN, Vaddi R, Manoharan P, Agilandeeswari L, Sangeetha V.   +4 more
europepmc   +1 more source

Generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing. [PDF]

Sci Rep
Jia Y, Yu W, Zhao L.
europepmc   +1 more source

Pure data correction enhancing remote sensing image classification with a lightweight ensemble model. [PDF]

Sci Rep
Song H, Xie H, Duan Y, Xie X, Gan F, Wang W, Liu J.   +6 more
europepmc   +1 more source

A Review of Remote Sensing Image Dehazing [PDF]

Sensors, 2021
Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently,
Dengyin Zhang, Mingye Ju, Zhang Dengyin   +2 more
exaly   +4 more sources

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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
openaire   +1 more source

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
openaire   +1 more source