scholarly journals Application of high frequency filtration to remove cloudiness in Earth remote sensing images

2020 ◽  
Vol 223 ◽  
pp. 02010
Author(s):  
Valeriy Tutatchikov ◽  
Mikhail Noskov
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Image Enhancement ◽  
High Frequency ◽  
Digital Processing ◽  
Remote Sensing Images ◽  
Earth Remote Sensing ◽  
High Clouds

At present, methods of digital processing of Earth remote sensing images are widely used to improve the image quality. For example, many images are discarded due to high clouds in the images, which obscure objects of interest. In this paper, the possibility of using high- frequency global filters to reduce cloudiness in the image is considered, and the results of image enhancement are shown.

A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

2016 ◽  
Vol 39 (2) ◽  
pp. 183-193 ◽  
Author(s):  
Lu Liu ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Contourlet Transform ◽  
Inverse Transformation ◽  
Unsharp Masking ◽  
Mean Filter ◽  
Enhancement Method

The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.

Automatic Airport Extraction Based on Improved Fuzzy Enhancement

2011 ◽  
Vol 130-134 ◽  
pp. 3421-3424
Author(s):  
Li Pan ◽  
Zhao Xian Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Enhancement ◽  
High Speed ◽  
Good Effect ◽  
Remote Sensing Images ◽  
Fuzzy Enhancement ◽  
Region Labeling ◽  
Strong Robustness ◽  
Enhancement Algorithm

We put forward a process of automatic airport extraction based on the characteristics of high resolution remote sensing images. First, through image enhancement algorithm, the contrast of target and background is enhanced. Second, we can extract the possible airport through the algorithms of Ostu segmentation, mathematical morphology corrosion and region-labeling. Finally, combined with the geometric structure of the runway, the airfield runway can be extracted through the algorithms of edge detection, progressive probability Hough transform and line connection. Then the possible airport can be verified by the extracted airfield runway. In the process, we proposed an improved fuzzy enhancement algorithm for image enhancement. This algorithm has good effect on the image enhancement and has strong robustness. The results of the experiment indicate that the process of automatic airport extraction is robust and has the advantages of high speed and degree of automation.

Remote Sensing Image Enhancement Based on Wavelet Transformation

2012 ◽  
Vol 198-199 ◽  
pp. 223-226
Author(s):  
Ying Zhao ◽  
Ye Cai Guo
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Theoretical Analysis ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Original Image ◽  
Remote Sensing Images ◽  
Enhancement Algorithm

The contrast of remote sensing images is very low, which include various noises. In order to make full used of remote sensing image information extraction and processing, the original image should have to be enhanced. In this paper the enhancement algorithm based on the biothogonal wavelet transform is proposed. Firstly, we have to eliminate the beforehand noise, and then take advantage of the non-linear wavelet transform to enhanced low-frequency and high- frequency coefficient respectively. Finally, the new picture is reconstruct by the transformed low-frequency and high-frequency coefficient. The efficiency of the proposed algorithm was proved by the theoretical analysis and computer simulations.

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.

Remote sensing imaging simulation and cloud removal

2017 ◽  
Vol 31 (16-19) ◽  
pp. 1744079 ◽  
Author(s):  
Xifang Zhu ◽  
Feng Wu ◽  
Tao Wu ◽  
Chunyu Zhao
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Simulation Method ◽  
Complex Wavelet Transform ◽  
Remote Sensing Images ◽  
Low Level ◽  
Cloud Removal ◽  
High Level

Cloud obstacles obscure ground information frequently during remote sensing imaging which leads to valuable information losses. Removing clouds from a single image becomes challenging since no reference images containing cloud-free regions are available. In order to study cloud removal technologies and evaluate their performances, a method to simulate evenly and unevenly distributed clouds was proposed by analyzing the physical model of remote sensing imaging. Dual tree complex wavelet transform (DTCWT) and its features were introduced briefly. According to the frequency relationships between clouds and ground objects in remote sensing images, a novel cloud removal algorithm was proposed. The algorithm divided the cloud-contaminated image into low-level high frequency sub-bands, high-level high frequency sub-bands and low frequency sub-band by DTCWT. Low-level high frequency sub-bands were filtered to enhance the ground object information by Laplacian sharpening. The other two types of sub-bands were processed to remove clouds by cloud cover coefficient weighting (CCCW). The experiments were implemented to process cloud disturbed images produced by the proposed simulation method. The results of cloud removal from remote sensing images were analyzed. It proved the proposed algorithm is greatly superior to algorithms based on traditional wavelet transform and dark channel prior.

JPEG2000-Based Optimization Algorithm for Effective Compression Display of Remote Sensing Images

2013 ◽  
Vol 325-326 ◽  
pp. 1602-1609 ◽  
Author(s):  
Liang Lu ◽  
Leng Zhang ◽  
Ting Zhang
Keyword(s):  
Remote Sensing ◽  
Optimization Algorithm ◽  
High Frequency ◽  
Frequency Component ◽  
Image Features ◽  
High Frequency Component ◽  
Remote Sensing Images ◽  
Jpeg2000 Compression ◽  
Bit Plane Coding ◽  
Roi Coding

Taking the compression issue of remote sensing images as the study subject, this paper analyses the technical process for JPEG2000 compression and the image features of remote sensing images, natural images and figural images, and puts forwards an integrative optimization algorithm for effective compression display for remote sensing images based on common JPEG2000 compression frame. Thereinto, it includes high-frequency component filtering treatment, parallel processing of bit plane coding pass scanning and improved ROI coding algorithm, which give obvious clews to both overall consumed time for image compression and the gradual display effect in ROI region.

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