Remote sensing image quality evaluation based on deep learning

2021 ◽  
pp. 1-9
Author(s):  
Tong Wang ◽  
Hemeng Yang ◽  
Ling Zhu ◽  
Yazhou Fan ◽  
Xue Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Quality Evaluation ◽  
Remote Sensing Image ◽  
Human Vision ◽  
Remote Sensing Images ◽  
Image Quality Evaluation ◽  
Remote Sensing Technology ◽  
Sensing Technology

Remote sensing technology is an effective tool for sensing the earth’s surface. With the continuous improvement of remote sensing technology, remote sensing detectors can obtain more spectral and spatial information, including clear feature contours, complex texture features and spatial layout rules. This information was detected in mineral resources. Surface substance identification, water pollution information monitoring and many other aspects have played an important role. The coding algorithm and defects, storage algorithm and interference from atmospheric cloud radiation information during the imaging process lead to varying degrees of distortion and deterioration of remote sensing images during imaging, transmission and storage. This makes it difficult to process, analyze and apply remote sensing images. Therefore, the design of a reasonable remote sensing image quality evaluation method is not only conducive to the remote sensing image quality evaluation in the real-time processing system of remote sensing image, but also conducive to the optimization of remote sensing image system and image processing algorithm. The application is worthwhile. In this paper, the deteriorating features of remote sensing images will change the statistical distribution. We propose a method for evaluating the quality of remote sensing images in depth learning. Feature learning and blurring as well as noise intensity classification for image remote sensing using convolutional neural network are carried out. The evaluation model is modified by masking effect and perceptual weighting factor, and the quality evaluation results of remote sensing images are obtained according to human vision. The research shows that this method can effectively solve the problem of removing and evaluating the noise of remote sensing image, and can effectively and accurately evaluate the quality of remote sensing image. It is also consistent with subjective assessment and human perception.

A CBIR System for Hyperspectral Remote Sensing Images Using Endmember Extraction

2017 ◽  
Vol 31 (04) ◽  
pp. 1752001 ◽  
Author(s):  
Jing Zhang ◽  
Qianlan Zhou ◽  
Li Zhuo ◽  
Wenhao Geng ◽  
Suyu Wang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Superior Performance ◽  
Endmember Extraction ◽  
Remote Sensing Images ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Hyperspectral Remote Sensing Images ◽  
Hyperspectral Remote Sensing Image

With the rapid development of remote sensing technology, searching the similar image is a challenge for hyperspectral remote sensing image processing. Meanwhile, the dramatic growth in the amount of hyperspectral remote sensing data has stimulated considerable research on content-based image retrieval (CBIR) in the field of remote sensing technology. Although many CBIR systems have been developed, few studies focused on the hyperspectral remote sensing images. A CBIR system for hyperspectral remote sensing image using endmember extraction is proposed in this paper. The main contributions of our method are that: (1) the endmembers as the spectral features are extracted from hyperspectral remote sensing image by improved automatic pixel purity index (APPI) algorithm; (2) the spectral information divergence and spectral angle match (SID–SAM) mixed measure method is utilized as a similarity measurement between hyperspectral remote sensing images. At last, the images are ranked with descending and the top-[Formula: see text] retrieved images are returned. The experimental results on NASA datasets show that our system can yield a superior performance.

The Research of Ortho-Rectification to QuickBird Image with more Mountains Based on ERDAS10.0

2014 ◽  
Vol 571-572 ◽  
pp. 772-776 ◽  
Author(s):  
Cong Li ◽  
Qiang Wang ◽  
Meng Wang ◽  
Jia Jie Cui
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Orthogonal Projection ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Positioning Accuracy ◽  
Remote Sensing Technology ◽  
Sensor Model ◽  
Sensing Technology

In recent years, the application of high resolution remote sensing images has become more and more widely with the development of Remote Sensing technology. QuickBird satellite image is the more commercial used high resolution remote sensing image, but due to its technical confidentiality, high-resolution satellite generally does not provide rigorous sensor model. This paper uses ERDAS10.0 to orthographic check the QuickBird image by the method of orthorectification, introduces the method and procedure of the orthogonal projection like drawing, analysis the positioning accuracy.

The Study on Environmental Evolved Characteristic of Sandy Coast

2012 ◽  
Vol 226-228 ◽  
pp. 1170-1173
Author(s):  
Qi Peng Zhang ◽  
Xiao Qing Han ◽  
Jing Li ◽  
Jing Jing Zhao ◽  
Wei Biao Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Bohai Sea ◽  
Hebei Province ◽  
Coastal Current ◽  
Remote Sensing Images ◽  
Remote Sensing Technology ◽  
The North ◽  
Sensing Technology ◽  
Sandy Coast

In order to study the evolved characteristic of sandy coast in Hebei Province, the paper analyzed costal information by Remote Sensing technology from landform maps and remote sensing images from 1956 to 2007. It studied the evolvement characteristics and the reasons of sandy coast deeply. And it also analyzed the evolvement infections to the nearby coast of the sandy engineering. The results showed that the characteristic was erosion condition in sandy coast. There were several different evolved processing in different area from 1959 to 2007. In the region between Daihe River and Tazigou, the highest erosion speed was 3.45 m/a by the coastal current and wave between Daihe River and Yanghe River. The section was deposited into the ocean with the speed of 1.29 m/a by the cultivation ponds building in Bohai Sea farmland between the Yanghe River and Dapuhe River. In the region between Tazigou and Langwokou River, the beach had been eroded about 373 m with the speed of 13.32 m/a by 2007. And the section was eroded offshore more serious with the distance of 610 m and the speed of 21.79 m/a from the north of Luanhe River.In the region between Langwokou River and Daqinghe River, the average erosion distance was about 370 m with the speed of 13.21 m/a in Shegang sandbar. And it was eroded back to mainland about 164 m with the speed of 8.20 m/a. And it was about 504m with the speed of 18.00 m/a.

A Multi-Criteria Image Quality Evaluation Scheme

2013 ◽  
Vol 284-287 ◽  
pp. 2975-2979
Author(s):  
Yen Ching Chang ◽  
Chun Ming Chang ◽  
Liang Hwa Chen ◽  
Tung Jung Chan
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Quality Evaluation ◽  
Experimental Results ◽  
Human Vision ◽  
Effective Mechanism ◽  
Image Quality Evaluation ◽  
Evaluation Scheme ◽  
Grey Relational Grade ◽  
Grey Relational

It is difficult to objectively and quantitatively judge image quality by a single criterion, such as contrast. In general, excessive contrast enhancement easily leads to a loss of image quality. Thus, it easily gives a wrong evaluation to rank image quality according to contrast values. In order to achieve a consistent result with human vision perception, balancing multi-criteria will be a feasible approach. Therefore, we propose a multi-criteria image quality evaluation scheme for ranking seven existing contrast enhancement methods. The scheme applies four criteria to a newly proposed way of computing a grey relational grade (GRGd), called the consistent grey relational grade (CGRGd). Experimental results show that our proposed CGRGd do provides a very effective mechanism to choose the best method for a specific purpose.

scholarly journals Exploring the Application of UAV Remote Sensing Technology in Geo-logical Hazard Survey

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Lili Chen
Keyword(s):  
Remote Sensing ◽  
Prevention And Control ◽  
Science And Technology ◽  
Emerging Technology ◽  
Disaster Prevention ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Geological Disaster ◽  
And Control

: Under the background of large science and technology, UAV remote sensing technology, as an emerging technology, has been widely applied in many fields and has improved the overall quality of geological disaster prevention and control. Therefore, surveying and mapping departments need to pay attention to the application of UAV remote sensing technology in geological disaster investigation.

scholarly journals Local Deep Descriptor for Remote Sensing Image Feature Matching

2019 ◽  
Vol 11 (4) ◽  
pp. 430 ◽  
Author(s):  
Yunyun Dong ◽  
Weili Jiao ◽  
Tengfei Long ◽  
Lanfa Liu ◽  
Guojin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computer Vision ◽  
Deep Learning ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Image Feature ◽  
Training Dataset ◽  
Feature Descriptor ◽  
Remote Sensing Images

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network.

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