Remote sensing in mineral exploration—really a practical tool?: image processing and GIS applications in exploration projects

1990 ◽  
pp. 259-262
Author(s):  
Enrique Ortega ◽  
Jesús Artieda ◽  
Rupert Haydn ◽  
Peter Volk
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Mineral Exploration ◽  
Gis Applications ◽  
Practical Tool

Introduction to the Special Issue on remote sensing

Geophysics ◽  
1987 ◽  
Vol 52 (7) ◽  
pp. 839-840
Author(s):  
Kenneth Watson
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Near Infrared ◽  
Gamma Ray ◽  
Mineral Exploration ◽  
Remote Sensing Data ◽  
Gamma Ray Spectrometry ◽  
Special Issue ◽  
Spectral Signatures ◽  
Uranium Exploration

In 1977, the first Special Issue on remote sensing published by Geophysics contained papers selected from two special sessions at the 45th Annual International SEG Meeting, October 12–16, 1975, in Denver, Colorado. That first Special Issue consisted of eight papers: four are primarily tutorial (image processing, spectral signatures in the visible and near infrared, microwave spectra of layered media, and factor analysis of gamma‐ray spectrometry), two involve structural interpretations with implications for mineral exploration and seismicity, and two examine multispectral reflectance data for detecting hydrothermal alteration and for uranium exploration. Although these papers indicate the importance of physical properties and models in the interpretation of remote sensing data, the studies were constrained by the instruments that collected the data and by the availability of image‐processing software. Circumstances have changed significantly in the intervening decade, as illustrated in recent review papers (Watson, 1985; Goetz et al., 1983) and demonstrated by the papers in this Special Issue.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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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