Matching SAR image to optical image using modified Hausdorff distance and genetic algorithms

2007 ◽  
Author(s):  
Haicen Mao ◽  
Qiuze Yu ◽  
Tianxu Zhang
Keyword(s):  
Genetic Algorithms ◽  
Hausdorff Distance ◽  
Optical Image ◽  
Sar Image ◽  
Modified Hausdorff Distance

scholarly journals MULTI-DATE SENTINEL1 SAR IMAGE TEXTURES DISCRIMINATE PERENNIAL AGROFORESTS IN A TROPICAL FOREST-SAVANNAH TRANSITION LANDSCAPE

2018 ◽  
Vol XLII-1 ◽  
pp. 339-346 ◽  
Author(s):  
F. N. Numbisi ◽  
F. Van Coillie ◽  
R. De Wulf
Keyword(s):  
Land Cover ◽  
Vegetation Indices ◽  
Image Resolution ◽  
Optical Image ◽  
Congo Basin ◽  
Sar Image ◽  
Radar Images ◽  
Tropical Conditions ◽  
Cocoa Agroforestry ◽  
Occurrence Matrix

<p><strong>Abstract.</strong> Synthetic Aperture Radar (SAR) provides consistent information on target land features; especially in tropical conditions that restrain penetration of optical imaging sensors. Because radar response signal is influenced by geometric and di-electrical properties of surface features’, the different land cover may appear similar in radar images. For discriminating perennial cocoa agroforestry land cover, we compare a multi-spectral optical image from RapidEye, acquired in the dry season, and multi-seasonal C-band SAR of Sentinel 1: A final set of 10 (out of 50) images that represent six dry and four wet seasons from 2015 to 2017. We ran eight RF models for different input band combinations; multi-spectral reflectance, vegetation indices, co-(VV) and cross-(VH) polarised SAR intensity and Grey Level Co-occurrence Matrix (GLCM) texture measures. Following a pixel-based image analysis, we evaluated accuracy metrics and uncertainty Shannon entropy. The model comprising co- and cross-polarised texture bands had the highest accuracy of 88.07<span class="thinspace"></span>% (95<span class="thinspace"></span>% CI: 85.52&amp;ndash;90.31) and kappa of 85.37; and the low class uncertainty for perennial agroforests and transition forests. The optical image had low classification uncertainty for the entire image; but, it performed better in discriminating non-vegetated areas. The measured uncertainty provides reliable validation for comparing class discrimination from different image resolution. The GLCM texture measures that are crucial in delineating vegetation cover differed for the season and polarization of SAR image. Given the high accuracies of mapping, our approach has value for landscape monitoring; and, an improved valuation of agroforestry contribution to REDD+ strategies in the Congo basin sub-region.</p>

Binary Image Recognition Utilizing Computer Generated Templates

2017 ◽  
Author(s):  
Dustin Bielecki ◽  
Prakhar Jaiswal ◽  
Rahul Rai
Keyword(s):  
Image Recognition ◽  
Hausdorff Distance ◽  
Binary Image ◽  
Sorting Technique ◽  
Milling Operation ◽  
Modified Hausdorff Distance ◽  
Image Templates

This paper covers a method of taking images of physical parts which are then preprocessed and compared against CAD generated templates. A pseudo milling operation was performed on discretized points along CAD generated mill paths to create binary image templates. The computer-generated images were then tested against one another as a preliminarily sorting technique. This was done to reduce the number of sorting approaches used, by selecting the most reliable and discerning ones, and discarding the others. To apply the selected sorting methods for comparing CAD generated images and the images of physical parts, a translational and scaling normalization technique was implemented. Rotational variation occurs while scanning physical parts and it was addressed using two different techniques: first by determination of best rotation based on modified-Hausdorff distance (MHD); and second by comparing against all CAD based images for all template rotations. The proposed approach for automated sorting of physical parts was demonstrated by categorizing multiple geometries.

scholarly journals Fully automatic segmentation of bee wing images

2020 ◽  
Vol 12 (2) ◽  
pp. 37-45
Author(s):  
João Marcos Garcia Fagundes ◽  
Allan Rodrigues Rebelo ◽  
Luciano Antonio Digiampietri ◽  
Helton Hideraldo Bíscaro
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Feature Extraction ◽  
Hausdorff Distance ◽  
Automatic Segmentation ◽  
Food Crops ◽  
Artificial Intelligence Techniques ◽  
Fully Automatic ◽  
Modified Hausdorff Distance ◽  
F Measure

Bee preservation is important because approximately 70% of all pollination of food crops is made by them and this service costs more than $ 65 billion annually. In order to help this preservation, the identification of the bee species is necessary, and since this is a costly and time-consuming process, techniques that automate and facilitate this identification become relevant. Images of bees' wings in conjunction with computer vision and artificial intelligence techniques can be used to automate this process. This paper presents an approach to do segmentation of bees' wing images and feature extraction. Our approach was evaluated using the modified Hausdorff distance and F measure. The results were, at least, 24% more precise than the related approaches and the proposed approach was able to deal with noisy images.

Corrigendum to “A modified Hausdorff distance between fuzzy sets”

2002 ◽  
Vol 148 (1-4) ◽  
pp. 233-234 ◽  
Author(s):  
Kiran R. Bhutani ◽  
B.B. Chaudhuri ◽  
Azriel Rosenfeld
Keyword(s):  
Fuzzy Sets ◽  
Hausdorff Distance ◽  
Modified Hausdorff Distance

Hausdorff Distance for Face Recognition

2015 ◽  
pp. 39-46
Keyword(s):  
Face Recognition ◽  
Hausdorff Distance ◽  
Similarity Measures ◽  
Face Verification ◽  
3D Data ◽  
Modified Hausdorff Distance

When two sets are differently sized, the Hausdorff distance can be computed between them, even if the cardinality of one set is infinite. Different versions of this distance have been proposed and employed for face verification, among which the modified Hausdorff distance is the most famous. The important point to be noted is that, among the most commonly used similarity measures, the Hausdorff distance is the only one that has been widely applied to 3D data.

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