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.

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.

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.

Methods of removal wide-stripe noise in short-wave infrared hyperspectral remote sensing image

Sensor Review ◽  
2019 ◽  
Vol 39 (1) ◽  
pp. 17-23 ◽  
Author(s):  
Shi-Qi Huang ◽  
Wen-Sheng Wu ◽  
Li-Ping Wang ◽  
Xiang-Yang Duan
Keyword(s):  
Remote Sensing ◽  
Short Wave ◽  
Hyperspectral Remote Sensing ◽  
Moment Matching ◽  
Remote Sensing Images ◽  
Content Type ◽  
Short Wave Infrared ◽  
Hyperspectral Remote Sensing Images ◽  
The Impact ◽  
Hyperspectral Remote Sensing Image

Purpose This paper aims to study the removal of wide-stripe noise in hyperspectral remote sensing images. There is a great deal of stripe noises in short-wave infrared hyperspectral remote sensing image, especially wide-stripe noise, which brings great challenge to the interpretation and application of hyperspectral images. Design/methodology/approach To remove the noise and to reduce the impact based on in-depth study of the mechanism of the stripe noise generation and its distribution characteristics, this paper proposed two statistical local processing and moment matching algorithms for the elimination of wide-stripe noise, namely, the gradient mean moment matching (GMMM) algorithm and the gradient interpolation moment matching (GIMM) algorithm. Findings The experiments were carried out with the practical short-wave infrared hyperspectral image data and good experiment results were obtained. Experiments show that both can reduce the impact of wide-stripe noise, and the filtering effect and the application range of the GIMM algorithm is better than that of the GMMM algorithm. Originality/value Using new methods to deal with the hyperspectral remote sensing images, it can effectively improve the quality of hyperspectral images and improve their utilization efficiency and value.

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