scholarly journals Spatial Scale Effect of a Typical Polarized Remote Sensor on Detecting Ground Objects

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4418
Author(s):  
Ying Zhang ◽  
Jingyi Sun ◽  
Rudong Qiu ◽  
Huilan Liu ◽  
Xi Zhang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Spatial Scale ◽  
Scale Effect ◽  
Convex Sets ◽  
Spatial Scales ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Mixed Pixel ◽  
Resolution Image ◽  
Remote Sensor

For polarized remote sensors, the polarization images of ground objects acquired at different spatial scales will be different due to the spatial heterogeneity of the ground object targets and the limitation of imaging resolution. In this paper, the quantitative inversion problem of a typical polarized remote sensor at different spatial scales was studied. Firstly, the surface roughness of coatings was inversed based on the polarized bidirectional reflectance distribution function (pBRDF) model according to their polarization images at different distances. A linear-mixed pixel model was used to make a preliminary correction of the spatial scale effect. Secondly, the super-resolution image reconstruction of the polarization imager was realized based on the projection onto convex sets (POCS) method. Then, images with different resolutions at a fixed distance were obtained by utilizing this super-resolution image reconstruction method and the optimal spatial scale under the scene can be acquired by using information entropy as an evaluation indicator. Finally, the experimental results showed that the roughness inversion of coatings has the highest accuracy in the optimal spatial scale. It has been proved that our proposed method can provide a reliable way to reduce the spatial effect of the polarized remote sensor and to improve the inversion accuracy.

MAP Based Super-Resolution Image Reconstruction Method

2012 ◽  
Vol 220-223 ◽  
pp. 2754-2757
Author(s):  
Pan Li He ◽  
Bo Yang Wang ◽  
Xiao Xia Liu ◽  
Xiao Wei Han
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Registration Accuracy ◽  
Resolution Image ◽  
Research Fields ◽  
Reconstruction Image ◽  
Active Research ◽  
High Resolution Reconstruction

Super-resolution image reconstruction has been one of the most active research fields in recent years. In this paper, a new super-resolution algorithm is proposed to the problem of obtaining a high-resolution image from several low- resolution images that have been sub sampled. In the image registration, the paper puts forward an improved search strategies improving registration accuracy. In the MAP algorithm, the threshold parameters of solving the optimal value, making the estimated value of the optimal high-resolution images, so that the reconstructed image is better. The results of the experiments indicate that the proposed algorithm can not only make an automatic choice of the parameter and get the high resolution reconstruction image expected, but also can preserve the edges and details of the image effectively.

scholarly journals Alternating Direction Projections onto Convex Sets for Super-Resolution Image Reconstruction

2020 ◽  
Vol 49 (1) ◽  
pp. 179-190
Author(s):  
Bin Zhou ◽  
Dong-jun Ye ◽  
Wei Wei ◽  
Marcin Wozniak
Keyword(s):  
Computer Vision ◽  
Image Reconstruction ◽  
Numerical Experiments ◽  
Convex Sets ◽  
Super Resolution ◽  
Experimental Results ◽  
Gradient Field ◽  
Resolution Image ◽  
Alternating Direction ◽  
Proposed Model

Image reconstruction is important in computer vision and many technologies have been presented to achieve better results. In this paper, gradient information is introduced to define new convex sets. A novel POCS-based model is proposed for super resolution reconstruction. The projection on the convex sets is alternative according to the gray value field and the gradient field. Then the local noise estimation is introduced to determine the threshold adaptively. The efficiency of our proposed model is verified by several numerical experiments. Experimental results show that, the PSNR and the SSIM can be both significantly improved by the proposed model.

scholarly journals Sub pixel location identification using super resolved multilooking CHRIS data

2014 ◽  
Vol XL-8 ◽  
pp. 463-468
Author(s):  
V. S. Sahithi ◽  
S. Agrawal
Keyword(s):  
High Resolution ◽  
Convex Sets ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Spectral Unmixing ◽  
Support Vector ◽  
Mixed Pixel ◽  
Resolution Image ◽  
High Resolution Image ◽  
Mode 3

CHRIS /Proba is a multiviewing hyperspectral sensor that monitors the earth in five different zenith angles +55°, +36°, nadir, −36° and −55° with a spatial resolution of 17 m and within a spectral range of 400–1050 nm in mode 3. These multiviewing images are suitable for constructing a super resolved high resolution image that can reveal the mixed pixel of the hyperspectral image. In the present work, an attempt is made to find the location of various features constituted within the 17m mixed pixel of the CHRIS image using various super resolution reconstruction techniques. Four different super resolution reconstruction techniques namely interpolation, iterative back projection, projection on to convex sets (POCS) and robust super resolution were tried on the −36, nadir and +36 images to construct a super resolved high resolution 5.6 m image. The results of super resolution reconstruction were compared with the scaled nadir image and bicubic convoluted image for comparision of the spatial and spectral property preservance. A support vector machine classification of the best super resolved high resolution image was performed to analyse the location of the sub pixel features. Validation of the obtained results was performed using the spectral unmixing fraction images and the 5.6 m classified LISS IV image.

Study on Application of Super-Resolution Image Reconstruction Method Used in Video Image Processing

2012 ◽  
Vol 476-478 ◽  
pp. 1142-1145
Author(s):  
Jing Jia Qi ◽  
Chuan Jun Guo ◽  
Yang Nan
Keyword(s):  
Image Reconstruction ◽  
Performance Improvement ◽  
Super Resolution ◽  
Imaging Systems ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Resolution Image ◽  
Image Reconstruction Method ◽  
Significant Performance ◽  
Resolution Imaging

Super resolution image reconstruction is a computational process of using multiple low-resolution observations to reconstruct a higher resolution image, which differs from improvement of optical devices. With magnification diversity among those low-resolution imagers, significant performance improvement, compared to traditional methods, is demonstrated. Results include fidelity metrics and simulated reconstructions. Performance improvement of super-resolution imaging systems with magnification diversity is studied in this paper.

Sign in / Sign up

Export Citation Format

Share Document