CNN-Based Dense Image Matching for Aerial Remote Sensing Images

2019 ◽  
Vol 85 (6) ◽  
pp. 415-424
Author(s):  
Shunping Ji ◽  
Jin Liu ◽  
Meng Lu
Keyword(s):  
Remote Sensing ◽  
Image Matching ◽  
Remote Sensing Images ◽  
Dense Image ◽  
Aerial Remote Sensing

scholarly journals Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

2020 ◽  
Vol 12 (4) ◽  
pp. 696 ◽  
Author(s):  
Zhen Ye ◽  
Yusheng Xu ◽  
Hao Chen ◽  
Jingwei Zhu ◽  
Xiaohua Tong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Comparative Study ◽  
Image Matching ◽  
Systematic Errors ◽  
Disparity Estimation ◽  
Sensing Applications ◽  
Subpixel Accuracy ◽  
Dense Image ◽  
Remote Sensing Applications ◽  
Practical Analysis

Dense image matching is a crucial step in many image processing tasks. Subpixel accuracy and fractional measurement are commonly pursued, considering the image resolution and application requirement, especially in the field of remote sensing. In this study, we conducted a practical analysis and comparative study on area-based dense image matching with subpixel accuracy for remote sensing applications, with a specific focus on the subpixel capability and robustness. Twelve representative matching algorithms with two types of correlation-based similarity measures and seven types of subpixel methods were selected. The existing matching algorithms were compared and evaluated in a simulated experiment using synthetic image pairs with varying amounts of aliasing and two real applications of attitude jitter detection and disparity estimation. The experimental results indicate that there are two types of systematic errors: displacement-dependent errors, depending on the fractional values of displacement, and displacement-independent errors represented as unexpected wave artifacts in this study. In addition, the strengths and limitations of different matching algorithms on the robustness to these two types of systematic errors were investigated and discussed.

scholarly journals Geo-Location Algorithm for Building Targets in Oblique Remote Sensing Images Based on Deep Learning and Height Estimation

2020 ◽  
Vol 12 (15) ◽  
pp. 2427
Author(s):  
Yiming Cai ◽  
Yalin Ding ◽  
Hongwen Zhang ◽  
Jihong Xiu ◽  
Zhiming Liu
Keyword(s):  
Remote Sensing ◽  
Simulation Analysis ◽  
Recognition Algorithm ◽  
Positioning Error ◽  
Remote Sensing Images ◽  
Target Height ◽  
Building Recognition ◽  
Aerial Remote Sensing ◽  
The Difference ◽  
Positioning Algorithm

To improve the accuracy of the geographic positioning of a single aerial remote sensing image, the height information of a building in the image must be considered. Oblique remote sensing images are essentially two-dimensional images and produce a large positioning error if a traditional positioning algorithm is used to locate the building directly. To address this problem, this study uses a convolutional neural network to automatically detect the location of buildings in remote sensing images. Moreover, it optimizes an automatic building recognition algorithm for oblique aerial remote sensing images based on You Only Look Once V4 (YOLO V4). This study also proposes a positioning algorithm for the building target, which uses the imaging angle to estimate the height of a building, and combines the spatial coordinate transformation matrix to calculate high-accuracy geo-location of target buildings. Simulation analysis shows that the traditional positioning algorithm inevitably leads to large errors in the positioning of building targets. When the target height is 50 m and the imaging angle is 70°, the positioning error is 114.89 m. Flight tests show that the algorithm established in this study can improve the positioning accuracy of building targets by approximately 20%–50% depending on the difference in target height.

scholarly journals Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2335 ◽  
Author(s):  
Yuelei Xu ◽  
Mingming Zhu ◽  
Peng Xin ◽  
Shuai Li ◽  
Min Qi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
False Alarm ◽  
Feature Fusion ◽  
Deep Layer ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Fully Convolutional Neural Networks ◽  
Aerial Remote Sensing ◽  
Detection Speed

To address the issues encountered when using traditional airplane detection methods, including the low accuracy rate, high false alarm rate, and low detection speed due to small object sizes in aerial remote sensing images, we propose a remote sensing image airplane detection method that uses multilayer feature fusion in fully convolutional neural networks. The shallow layer and deep layer features are fused at the same scale after sampling to overcome the problems of low dimensionality in the deep layer and the inadequate expression of small objects. The sizes of candidate regions are modified to fit the size of the actual airplanes in the remote sensing images. The fully connected layers are replaced with convolutional layers to reduce the network parameters and adapt to different input image sizes. The region proposal network shares convolutional layers with the detection network, which ensures high detection efficiency. The simulation results indicate that, when compared to typical airplane detection methods, the proposed method is more accurate and has a lower false alarm rate. Additionally, the detection speed is considerably faster and the method can accurately and rapidly complete airplane detection tasks in aerial remote sensing images.

Image matching using structural similarity and geometric constraint approaches on remote sensing images

2016 ◽  
Vol 10 (4) ◽  
pp. 045007 ◽  
Author(s):  
Jian-hua Guo ◽  
Fan Yang ◽  
Hai Tan ◽  
Jing-xue Wang ◽  
Zhi-heng Liu
Keyword(s):  
Remote Sensing ◽  
Image Matching ◽  
Structural Similarity ◽  
Geometric Constraint ◽  
Remote Sensing Images

A Multi-Level Approach for Change Detection of Buildings Using Satellite Imagery

2018 ◽  
Vol 27 (08) ◽  
pp. 1850031 ◽  
Author(s):  
Md. Abdul Alim Sheikh ◽  
Alok Kole ◽  
Tanmoy Maity
Keyword(s):  
Remote Sensing ◽  
Mutual Information ◽  
Change Detection ◽  
Satellite Imagery ◽  
Image Matching ◽  
Google Earth ◽  
Remote Sensing Images ◽  
Normalized Mutual Information ◽  
Novel Technique ◽  
Multi Level

In this paper a novel technique for building change detection from remote sensing imagery is presented. It includes two main stages: (1) Object-specific discriminative features are extracted using Morphological Building Index (MBI) to automatically detect the existence of buildings in remote sensing images. (2) Pixel-based image matching is measured on the basis of Mutual Information (MI) of the images by Normalized Mutual Information (NMI). Here, the MBI features values are computed for each of the pair images taken over the same region at two different times and then changes in these two MBI images are measured to indicate the building change. MI is estimated locally for all the pixels for image matching and then thresholding is applied for eliminating those pixels which are responsible for strong similarity. Finally, after getting the MBI and NMI images, a further fusion of these two images is done for refinement of the change result. For evaluation purpose, the experiments are carried on QuickBird, IKONOS images and images taken from Google Earth. The results show that the proposed technique can attain acceptable correctness rates above 90% with Overall Accuracy (OA) 89.52%.

A robust framework for quality enhancement of aerial remote sensing images

2018 ◽  
Vol 93 ◽  
pp. 362-374 ◽  
Author(s):  
Eerapu Karuna Kumari ◽  
Devikalyan Das ◽  
Shilpa Suresh ◽  
Shyam Lal ◽  
A.V. Narasimhadhan
Keyword(s):  
Remote Sensing ◽  
Quality Enhancement ◽  
Remote Sensing Images ◽  
Aerial Remote Sensing ◽  
Robust Framework

scholarly journals PHOTOGRAMMETRIC PROCESSING OF PLANETARY LINEAR PUSHBROOM IMAGES BASED ON APPROXIMATE ORTHOPHOTOS

2018 ◽  
Vol XLII-3 ◽  
pp. 391-396
Author(s):  
X. Geng ◽  
Q. Xu ◽  
S. Xing ◽  
Y. F. Hou ◽  
C. Z. Lan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Matching ◽  
Digital Terrain Model ◽  
Search Space ◽  
Bundle Adjustment ◽  
Projection Algorithm ◽  
Conjugate Points ◽  
Generation Process ◽  
Remote Sensing Images ◽  
Lunar Reconnaissance Orbiter

It is still a great challenging task to efficiently produce planetary mapping products from orbital remote sensing images. There are many disadvantages in photogrammetric processing of planetary stereo images, such as lacking ground control information and informative features. Among which, image matching is the most difficult job in planetary photogrammetry. This paper designs a photogrammetric processing framework for planetary remote sensing images based on approximate orthophotos. Both tie points extraction for bundle adjustment and dense image matching for generating digital terrain model (DTM) are performed on approximate orthophotos. Since most of planetary remote sensing images are acquired by linear scanner cameras, we mainly deal with linear pushbroom images. In order to improve the computational efficiency of orthophotos generation and coordinates transformation, a fast back-projection algorithm of linear pushbroom images is introduced. Moreover, an iteratively refined DTM and orthophotos scheme was adopted in the DTM generation process, which is helpful to reduce search space of image matching and improve matching accuracy of conjugate points. With the advantages of approximate orthophotos, the matching results of planetary remote sensing images can be greatly improved. We tested the proposed approach with Mars Express (MEX) High Resolution Stereo Camera (HRSC) and Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) images. The preliminary experimental results demonstrate the feasibility of the proposed approach.

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