A Hybrid Dense Matching Method for Satellite Images

2013 ◽  
Vol 726-731 ◽  
pp. 4547-4551
Author(s):  
Sheng Hua Teng ◽  
Ning Yang
Keyword(s):  
Satellite Images ◽  
Matching Problem ◽  
Canny Operator ◽  
Dense Matching ◽  
Edge Points ◽  
Feature Based ◽  
Matching Element ◽  
Grid Points ◽  
Geometrically Constrained ◽  
Image Pairs

To solve the dense matching problem for stereoscopic satellite images, a hybrid matching scheme integrating multiple methods is proposed. This scheme utilizes two types of matching element including grid points and edge points. First the geometrically constrained cross-correlation (GC3) method is used to extract matching grids. While in less textured regions using GC3 cannot get sufficient matching grids, so the local affine transformation is used to establish the region correspondence, and more matching grids can be generated. Edges are extracted by Canny operator and approximated with a series of straight edge segments using a polygon approximation. Based on these approximated edges, edge correspondences between image pairs are established using GC3. This scheme fuses the region based and the feature based matching methods. It has been tested with real satellite images and the results demonstrate its accuracy and efficiency.

scholarly journals An SRTM-Aided Epipolar Resampling Method for Multi-Source High-Resolution Satellite Stereo Observation

2019 ◽  
Vol 11 (6) ◽  
pp. 678
Author(s):  
Jingwen Hu ◽  
Gui-Song Xia ◽  
Hong Sun
Keyword(s):  
Satellite Images ◽  
Stereo Matching ◽  
Shuttle Radar Topography Mission ◽  
Image Space ◽  
Source Image ◽  
Resampling Method ◽  
Binocular Stereo ◽  
Grid Points ◽  
Image Pairs ◽  
Homography Transformation

Binocular stereo observation with multi-source satellite images used to be challenging and impractical, but is now a valuable research issue with the introduction of powerful deep-learning-based stereo matching approaches. However, epipolar resampling, which is critical for binocular stereo observation, has rarely been studied with multi-source satellite images. The main problem is that, under the multi-source stereo mode, the epipolar-line-direction (ELD) at an image location may vary when computed with different elevations. Thus, a novel SRTM (Shuttle Radar Topography Mission)-aided approach is proposed, where a point is transformed from the original image-space to the epipolar image-space through a global rotation, followed by a block-wise homography transformation. The global rotation transfers the ELDs at the center of the overlapping area to the x-axis, and then block-wise transformation shifts the ELDs of all grid-points to the x-axis and eliminates the y-disparities between the virtual corresponding points. Experiments with both single-source and multi-source stereo images showed that the proposed method is obviously more accurate than the previous methods that do not use SRTM. Moreover, with some of the multi-source image pairs, only the proposed method ensured the y-disparities remained within ±1 pixel.

scholarly journals True-orthophoto generation from UAV images: Implementation of a combined photogrammetric and computer vision approach

2014 ◽  
Vol II-5 ◽  
pp. 57-63 ◽  
Author(s):  
L. Barazzetti ◽  
R. Brumana ◽  
D. Oreni ◽  
M. Previtali ◽  
F. Roncoroni
Keyword(s):  
3D Model ◽  
Bundle Block Adjustment ◽  
Dense Matching ◽  
Automated Data Processing ◽  
Feature Based ◽  
Correlation Techniques ◽  
Uav Images ◽  
Image Orientation ◽  
Santa Maria

This paper presents a photogrammetric methodology for true-orthophoto generation with images acquired from UAV platforms. The method is an automated multistep workflow made up of three main parts: (i) image orientation through feature-based matching and collinearity equations / bundle block adjustment, (ii) dense matching with correlation techniques able to manage multiple images, and true-orthophoto mapping for 3D model texturing. It allows automated data processing of sparse blocks of convergent images in order to obtain a final true-orthophoto where problems such as self-occlusions, ghost effects, and multiple texture assignments are taken into consideration. <br><br> The different algorithms are illustrated and discussed along with a real case study concerning the UAV flight over the Basilica di Santa Maria di Collemaggio in L'Aquila (Italy). The final result is a rigorous true-orthophoto used to inspect the roof of the Basilica, which was seriously damaged by the earthquake in 2009.

scholarly journals Monitoring Deformasi Gunung Merapi Menggunakan Citra Sentinel-1A Dengan Menggunakan Metode DInSAR (Studi Kasus: Gunung Merapi, Jawa Tengah)

2020 ◽  
Vol 4 (1) ◽  
pp. 14-23
Author(s):  
Rian Nurtyawan ◽  
Lady Suci Utami
Keyword(s):  
Digital Elevation Model ◽  
Volcanic Activity ◽  
Satellite Images ◽  
Disaster Mitigation ◽  
Deformation Method ◽  
Differential Interferometry ◽  
Digital Elevation ◽  
Image Pairs ◽  
Elevation Model ◽  
Deformation Map

ABSTRAKIndonesia mempunyai 127 gunung api aktif yang tersebar dari Sabang sampai Merauke. Oleh karena itu, perlu adanya pemantauan aktivitas gunung api yang dapat digunakan untuk acuan mitigasi bencana. Pada penelitian ini menggunakan metode deformasi, metode deformasi merupakan perubahan bentuk, posisi, dan dimensi dari suatu benda. Tujuan dari pemantauan deformasi ini untuk mengetahui perubahan gunung api yang disebabkan oleh aktivitas gunung api. Pemantauan aktivitas gunung api metode deformasi dilakukan dengan menggunakan citra Sentinel-1A yang diolah dengan teknologi Differential Interferometry SAR (DInSAR). Dalam penelitian ini dilakukan pengolahan dengan teknologi DInSAR metode two-pass dari empat buah citra satelit sentinel-1A 10 Januari 2018, 27 Februari 2018, 10 Mei 2018 dan 22 Januari 2019 serta data Digital Elevation Model (DEM) SRTM dengan ketelitian 30 meter .Hasil dari penelitian ini yaitu peta deformasi pra 1 erupsi yang diolah dari pasangan citra 10 Januari 2018 dengan citra 27 Februari 2018 yang menghasilkan deflasi sebesar -0,12 meter, dan peta deformasi pra 2 erupsi yang diolah dari pasangan citra 27 Februari 2018 dan 10 Mei 2018 menghasilkan deflasi sebesar -0,27 meter serta peta pasca erupsi yang diolah dari pasangan citra 10 Mei 3018 dan 22 Januari 2019 menghasilkan deflasi sebesar -0,194 meter.Kata kunci: Deformasi, Gunung Merapi, Sentinel-1A, DInSAR. ABSTRACT Indonesia has 127 active volcanoes spread over from Sabang to Merauke. Therefore, it is necessary to monitor volcanic activity that can be used as a reference for disaster mitigation. In this study, deformation method was used to reflect a change in the shape, position, and dimensions of an object. The purpose of monitoring this deformation is to find out volcanic changes caused by volcanic activity. Monitoring the volcanic activity of the deformation method is carried out using Sentinel-1A images processed with Differential Interferometry SAR (DInSAR) technology. In this research, two-pass method of DInSAR technology was processed using four sentinel-1A satellite images on January 10, 2018, February 27, 2018, May 10, 2018 and January 22, 2019 and SRTM Digital Elevation Model (DEM) data with 30 meters accuracy. This research processed pre-eruption deformation map from the 10 January 2018 imagery pair with the 27 February 2018 image which resulted in a deflation of 0.12 meters. Pre- eruption 2 deformation map was processed from the 27 February 2018 and 10 May 2018 image pairs and resulted in a deflation of 0.27 meters while post-eruption map processed from the 10 May 3018 and 22 January 2019 image pairs resulted in deflation of 0.194 meters.Keywords: Deformation, Merapi Mountain, Sentinel-1A, DinSAR.

scholarly journals Efficient Segmentation and Registration of Retinal Image Using Gumble Probability Distribution and BRISK Feature

2020 ◽  
Vol 37 (5) ◽  
pp. 855-864
Author(s):  
Nagendra Pratap Singh ◽  
Vibhav Prakash Singh
Keyword(s):  
Retinal Image ◽  
Matched Filter ◽  
Computation Time ◽  
Retinal Vessel ◽  
Retinal Images ◽  
Feature Descriptor ◽  
Retinal Diseases ◽  
Vessel Structure ◽  
Feature Based ◽  
Image Pairs

The registration of segmented retinal images is mainly used for the diagnosis of various diseases such as glaucoma, diabetes, and hypertension, etc. These retinal diseases depend on the retinal vessel structure. The fast and accurate registration of segmented retinal images helps to identify the changes in vessels and the diagnosis of the diseases. This paper presents a novel binary robust invariant scalable key point (BRISK) feature-based segmented retinal image registration approach. The BRISK framework is an efficient keypoint detection, description, and matching approach. The proposed approach contains three steps, namely, pre-processing, segmentation using matched filter based Gumbel pdf, and BRISK framework for registration of segmented source and target retinal images. The effectiveness of the proposed approach is demonstrated by evaluating the normalized cross-correlation of image pairs. Based on the experimental analysis, it has been observed that the performance of the proposed approach is better in both aspect, registration performance as well as computation time with respect to SURF and Harris partial intensity invariant feature descriptor based registration.

Feature Based Registration of Satellite Images

2007 ◽  
Author(s):  
Y. Bentoutou ◽  
N. Taleb ◽  
A. Bounoua ◽  
K. Kpalma ◽  
J. Ronsin
Keyword(s):  
Satellite Images ◽  
Feature Based

scholarly journals Efficient Image Registration for Underwater Optical Mapping Using Geometric Invariants

2019 ◽  
Vol 7 (6) ◽  
pp. 178
Author(s):  
Armagan Elibol ◽  
Nak Young Chong
Keyword(s):  
Image Registration ◽  
Optical Mapping ◽  
Probabilistic Methods ◽  
Computational Time ◽  
Intermediate Step ◽  
Geometrical Properties ◽  
Geometric Invariants ◽  
Salient Points ◽  
Feature Based ◽  
Image Pairs

Image registration is one of the most fundamental and widely used tools in optical mapping applications. It is mostly achieved by extracting and matching salient points (features) described by vectors (feature descriptors) from images. While matching the descriptors, mismatches (outliers) do appear. Probabilistic methods are then applied to remove outliers and to find the transformation (motion) between images. These methods work in an iterative manner. In this paper, an efficient way of integrating geometric invariants into feature-based image registration is presented aiming at improving the performance of image registration in terms of both computational time and accuracy. To do so, geometrical properties that are invariant to coordinate transforms are studied. This would be beneficial to all methods that use image registration as an intermediate step. Experimental results are presented using both semi-synthetically generated data and real image pairs from underwater environments.

scholarly journals Feature-Based Retinal Image Registration Using D-Saddle Feature

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Roziana Ramli ◽  
Mohd Yamani Idna Idris ◽  
Khairunnisa Hasikin ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab ◽  
...  
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Retinal Diseases ◽  
Feature Points ◽  
Registration Accuracy ◽  
Gaussian Pyramid ◽  
Difference Of Gaussian ◽  
Lower Success Rate ◽  
Feature Based ◽  
Image Pairs

Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

scholarly journals Edge Detection of Agricultural Products Based on Morphologically Improved Canny Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaokang Yu ◽  
Zhiwen Wang ◽  
Yuhang Wang ◽  
Canlong Zhang
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Agricultural Products ◽  
Canny Operator ◽  
Traditional Algorithm ◽  
Edge Points ◽  
Canny Edge ◽  
Image Edge ◽  
Canny Algorithm ◽  
Canny Edge Detection Algorithm

The traditional canny edge detection algorithm has its limitations in the aspect of antinoise interference, and it is susceptible to factors such as light. To solve these defects, the Canny algorithm based on morphological improvement was proposed and applied to the detection of agricultural products. First, the algorithm uses the open and close operation of morphology to form a morphological filter instead of the Gaussian filter, which can remove the image noise and strengthen the protection of image edge. Second, the traditional Canny operator is improved to increase the horizontal and vertical templates to 45° and 135° to improve the edge positioning of the image. Finally, the adaptive threshold segmentation method is used for rough segmentation, and on this basis, double detection thresholds are used for further segmentation to obtain the final edge points. The experimental results show that compared with the traditional algorithm applied to the edge detection of agricultural products, this algorithm can effectively avoid the false contour caused by illumination and other factors and effectively improve the antinoise interference while more accurate and fine detection of the edge of real agricultural products.

scholarly journals Fundus Image Registration Technique Based on Local Feature of Retinal Vessels

2021 ◽  
Vol 11 (23) ◽  
pp. 11201
Author(s):  
Roziana Ramli ◽  
Khairunnisa Hasikin ◽  
Mohd Yamani Idna Idris ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab
Keyword(s):  
Feature Extraction ◽  
Image Registration ◽  
Extraction Methods ◽  
Local Feature ◽  
Retinal Vessels ◽  
Feature Points ◽  
Fundus Image ◽  
Scale Invariant ◽  
Feature Based ◽  
Image Pairs

Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image may resemble retinal vessels in local patches. Therefore, this paper introduces a feature extraction method based on a local feature of retinal vessels (CURVE) that incorporates retinal vessels and noises characteristics to accurately extract feature points on retinal vessels and throughout the fundus image. The CURVE performance is tested on CHASE, DRIVE, HRF and STARE datasets and compared with six feature extraction methods used in the existing feature-based RIR techniques. From the experiment, the feature extraction accuracy of CURVE (86.021%) significantly outperformed the existing feature extraction methods (p ≤ 0.001*). Then, CURVE is paired with a scale-invariant feature transform (SIFT) descriptor to test its registration capability on the fundus image registration (FIRE) dataset. Overall, CURVE-SIFT successfully registered 44.030% of the image pairs while the existing feature-based RIR techniques (GDB-ICP, Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG) only registered less than 27.612% of the image pairs. The one-way ANOVA analysis showed that CURVE-SIFT significantly outperformed GDB-ICP (p = 0.007*), Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG (p ≤ 0.001*).

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