scholarly journals Seamline Determination Based on PKGC Segmentation for Remote Sensing Image Mosaicking

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1721 ◽  
Author(s):  
Qiang Dong ◽  
Jinghong Liu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Mosaicking

scholarly journals A review of parallel computing for large-scale remote sensing image mosaicking

2015 ◽  
Vol 18 (2) ◽  
pp. 517-529 ◽  
Author(s):  
Lajiao Chen ◽  
Yan Ma ◽  
Peng Liu ◽  
Jingbo Wei ◽  
Wei Jie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Parallel Computing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Image Mosaicking

scholarly journals Automatic Seam-Line Detection in UAV Remote Sensing Image Mosaicking by Use of Graph Cuts

2018 ◽  
Vol 7 (9) ◽  
pp. 361 ◽  
Author(s):  
Ming Li ◽  
Deren Li ◽  
Bingxuan Guo ◽  
Lin Li ◽  
Teng Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Function ◽  
Graph Cuts ◽  
Remote Sensing Image ◽  
Existing Problems ◽  
Image Mosaicking ◽  
Target Energy ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Uav Image

Image mosaicking is one of the key technologies in data processing in the field of computer vision and digital photogrammetry. For the existing problems of seam, pixel aliasing, and ghosting in mosaic images, this paper proposes and implements an optimal seam-line search method based on graph cuts for unmanned aerial vehicle (UAV) remote sensing image mosaicking. This paper first uses a mature and accurate image matching method to register the pre-mosaicked UAV images, and then it marks the source of each pixel in the overlapped area of adjacent images and calculates the energy value contributed by the marker by using the target energy function of graph cuts constructed in this paper. Finally, the optimal seam-line can be obtained by solving the minimum value of target energy function based on graph cuts. The experimental results show that our method can realize seamless UAV image mosaicking, and the image mosaic area transitions naturally.

Remote Sensing Image Mosaicking: Achievements and Challenges

2019 ◽  
Vol 7 (4) ◽  
pp. 8-22 ◽  
Author(s):  
Xinghua Li ◽  
Ruitao Feng ◽  
Xiaobin Guan ◽  
Huanfeng Shen ◽  
Liangpei Zhang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Mosaicking

Automated seamline generation along skeleton for remote sensing image mosaicking considering complex background

2016 ◽  
Vol 45 (s1) ◽  
pp. -1
Author(s):  
张汉松 Zhang Hansong ◽  
陈建裕 Chen Jianyu ◽  
侯淑涛 Hou Shutao ◽  
霍奕任 Huo Yiren
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Mosaicking ◽  
Complex Background

Automated seamline generation along skeleton for remote sensing image mosaicking considering complex background

2016 ◽  
Vol 45 (s1) ◽  
pp. -1
Author(s):  
张汉松 Zhang Hansong ◽  
陈建裕 Chen Jianyu ◽  
侯淑涛 Hou Shutao ◽  
霍奕任 Huo Yiren
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Mosaicking ◽  
Complex Background

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. 

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