Analysis and Performance Evaluation of Parameterization Algorithms in Remote Sensing Image Processing

2018 ◽  
Author(s):  
Edmore Chikohora ◽  
Bokuhwo M. Esiefarienrhe ◽  
Teressa T. Chikohora
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Performance Evaluation ◽  
Remote Sensing Image ◽  
And Performance ◽  
Remote Sensing Image Processing

Research and Performance Analysis on Parallel Computing of Remote Sensing Image Based on MPICH

2013 ◽  
Vol 680 ◽  
pp. 540-545
Author(s):  
Jun Li ◽  
Wei Feng Ma
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Parallel Computing ◽  
Cluster Computing ◽  
Single Mode ◽  
Remote Sensing Image ◽  
Distributed Parallel Computing ◽  
And Performance ◽  
Occurrence Matrix ◽  
Remote Sensing Image Processing

The traditional centralized single mode becomes a “bottleneck” of remote sensing image processing which cannot meet the needs of future remote sensing image processing development. Fortunately, the distributed parallel computing has provided a turning point to the quick calculation of remote sensing image. This paper presents the cluster computing environment based on the MPI, and advances a project of a parallelized design to the gray level co-occurrence matrix algorithm. Moreover, the experimental data, which is due to the parallelized algorithm running in the cluster, is recorded and analyzed in several respects such as different nodes, time, speedup, efficiency and so on. The analyzed result shows that parallel computing cluster based on MPICH can efficiently improve the speed of remote sensing image processing in the case of more complex algorithms. However, when the number of node increases, the consuming time decreases, and the efficiency will decrease at the same time. So, it is rather important to keep the balance between performance and efficiency. The nodes can not be continuously added into computing, when the consuming time can be accepted.

scholarly journals Remote Sensing Image Dataset Expansion Based on Generative Adversarial Networks with Modified Shuffle Attention

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4867
Author(s):  
Lu Chen ◽  
Hongjun Wang ◽  
Xianghao Meng
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Remote Sensing Image ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Evaluation Indexes ◽  
Adversarial Network ◽  
Remote Sensing Image Processing ◽  
Data Expansion

With the development of science and technology, neural networks, as an effective tool in image processing, play an important role in gradual remote-sensing image-processing. However, the training of neural networks requires a large sample database. Therefore, expanding datasets with limited samples has gradually become a research hotspot. The emergence of the generative adversarial network (GAN) provides new ideas for data expansion. Traditional GANs either require a large number of input data, or lack detail in the pictures generated. In this paper, we modify a shuffle attention network and introduce it into GAN to generate higher quality pictures with limited inputs. In addition, we improved the existing resize method and proposed an equal stretch resize method to solve the problem of image distortion caused by different input sizes. In the experiment, we also embed the newly proposed coordinate attention (CA) module into the backbone network as a control test. Qualitative indexes and six quantitative evaluation indexes were used to evaluate the experimental results, which show that, compared with other GANs used for picture generation, the modified Shuffle Attention GAN proposed in this paper can generate more refined and high-quality diversified aircraft pictures with more detailed features of the object under limited datasets.

A dynamic acceleration method for remote sensing image processing based on CUDA

2021 ◽  
Author(s):  
Xianyu Zuo ◽  
Zhe Zhang ◽  
Baojun Qiao ◽  
Junfeng Tian ◽  
Liming Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Remote Sensing Image ◽  
Acceleration Method ◽  
Remote Sensing Image Processing
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