A deep learning semantic template matching framework for remote sensing image registration

2021 ◽  
Vol 181 ◽  
pp. 205-217
Author(s):  
Liangzhi Li ◽  
Ling Han ◽  
Mingtao Ding ◽  
Hongye Cao ◽  
Huijuan Hu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Registration ◽  
Template Matching ◽  
Remote Sensing Image

Multi-Source Remote Sensing Image Registration Based on Local Deep Learning Feature

2021 ◽  
Author(s):  
Yongxian Zhang ◽  
Zhijun Zhang ◽  
Guorui Ma ◽  
Jiao Wu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Registration ◽  
Remote Sensing Image

Robust Multisource Remote Sensing Image Registration Method Based on Scene Shape Similarity

2019 ◽  
Vol 85 (10) ◽  
pp. 725-736 ◽  
Author(s):  
Ming Hao ◽  
Jian Jin ◽  
Mengchao Zhou ◽  
Yi Tian ◽  
Wenzhong Shi
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Similarity Measure ◽  
Template Matching ◽  
Remote Sensing Image ◽  
Shape Similarity ◽  
Remote Sensing Images ◽  
Registration Method ◽  
Sensing Applications ◽  
Global Consistency

Image registration is an indispensable component of remote sensing applications, such as disaster monitoring, change detection, and classification. Grayscale differences and geometric distortions often occur among multisource images due to their different imaging mechanisms, thus making it difficult to acquire feature points and match corresponding points. This article proposes a scene shape similarity feature (SSSF) descriptor based on scene shape features and shape context algorithms. A new similarity measure called SSSFncc is then defined by computing the normalized correlation coefficient of the SSSF descriptors between multisource remote sensing images. Furthermore, the tie points between the reference and the sensed image are extracted via a template matching strategy. A global consistency check method is then used to remove the mismatched tie points. Finally, a piecewise linear transform model is selected to rectify the remote sensing image. The proposed SSSFncc aims to extract the scene shape similarity between multisource images. The accuracy of the proposed SSSFncc is evaluated using five pairs of experimental images from optical, synthetic aperture radar, and map data. Registration results demonstrate that the SSSFncc similarity measure is robust enough for complex nonlinear grayscale differences among multisource remote sensing images. The proposed method achieves more reliable registration outcomes compared with other popular methods.

A deep learning framework for remote sensing image registration

2018 ◽  
Vol 145 ◽  
pp. 148-164 ◽  
Author(s):  
Shuang Wang ◽  
Dou Quan ◽  
Xuefeng Liang ◽  
Mengdan Ning ◽  
Yanhe Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Registration ◽  
Remote Sensing Image ◽  
Learning Framework

Remote Sensing Image Registration Based on Deep Learning Regression Model

2020 ◽  
pp. 1-5
Author(s):  
Liangzhi Li ◽  
Ling Han ◽  
Mingtao Ding ◽  
Zhiheng Liu ◽  
Hongye Cao
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Registration ◽  
Regression Model ◽  
Remote Sensing Image

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

scholarly journals Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2407
Author(s):  
Hojun You ◽  
Dongsu Kim
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Optical Flow ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Template Matching ◽  
Hyperspectral Images ◽  
Spatial Accuracy ◽  
Flow Algorithm ◽  
Fluvial Remote Sensing

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

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