Adaptive Regional Multiple Features for Large-Scale High Resolution Remote Sensing Image Registration

2022 ◽  
pp. 1-1
Author(s):  
Zezhou Li ◽  
Jun Yue ◽  
Leyuan Fang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Multiple Features

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

Multilevel similarity model for high-resolution remote sensing image registration

2019 ◽  
Vol 505 ◽  
pp. 294-305 ◽  
Author(s):  
Xianmin Wang ◽  
Jing Li ◽  
Jin Li ◽  
Hongyang Yan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Remote Sensing Image ◽  
Similarity Model

scholarly journals High-Resolution Optical Remote Sensing Image Registration via Reweighted Random Walk based Hyper-Graph Matching

2019 ◽  
Vol 11 (23) ◽  
pp. 2841 ◽  
Author(s):  
Wu ◽  
Di ◽  
Ming ◽  
Lv ◽  
Tan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Graph Matching ◽  
Remote Sensing Image ◽  
Feature Point ◽  
Optical Remote Sensing ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Optical Remote Sensing Image

High-resolution optical remote sensing image registration is still a challenging task due to non-linearity in the intensity differences and geometric distortion. In this paper, an efficient method utilizing a hyper-graph matching algorithm is proposed, which can simultaneously use the high-order structure information and radiometric information, to obtain thousands of feature point pairs for accurate image registration. The method mainly consists of the following steps: firstly, initial matching by Uniform Robust Scale-Invariant Feature Transform (UR-SIFT) is carried out in the highest pyramid image level to derive the approximate geometric relationship between the images; secondly, two-stage point matching is performed to find the matches, that is, a rotation and scale invariant area-based matching method is used to derive matching candidates for each feature point and an efficient hyper-graph matching algorithm is applied to find the best match for each feature point; thirdly, a local quadratic polynomial constraint framework is used to eliminate match outliers; finally, the above process is iterated until finishing the matching in the original image. Then, the obtained correspondences are used to perform the image registration. The effectiveness of the proposed method is tested with six pairs of high-resolution optical images, covering different landscape types—such as mountain area, urban, suburb, and flat land—and registration accuracy of sub-pixel level is obtained. The experiments show that the proposed method outperforms the conventional matching algorithms such as SURF, AKAZE, ORB, BRISK, and FAST in terms of total number of correct matches and matching precision.

scholarly journals APPLICATION OF MULTI-SOURCE REMOTE SENSING IMAGE IN YUNNAN PROVINCE GRASSLAND RESOURCES INVESTIGATION

2018 ◽  
Vol XLII-3 ◽  
pp. 837-841
Author(s):  
J. Li ◽  
G. Wen ◽  
D. Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Yunnan Province ◽  
Remote Sensing Image ◽  
Field Investigation ◽  
Background Information ◽  
Landsat 8 ◽  
Yield Model ◽  
Wide Range

Trough mastering background information of Yunnan province grassland resources utilization and ecological conditions to improves grassland elaborating management capacity, it carried out grassland resource investigation work by Yunnan province agriculture department in 2017. The traditional grassland resource investigation method is ground based investigation, which is time-consuming and inefficient, especially not suitable for large scale and hard-to-reach areas. While remote sensing is low cost, wide range and efficient, which can reflect grassland resources present situation objectively. It has become indispensable grassland monitoring technology and data sources and it has got more and more recognition and application in grassland resources monitoring research. This paper researches application of multi-source remote sensing image in Yunnan province grassland resources investigation. First of all, it extracts grassland resources thematic information and conducts field investigation through BJ-2 high space resolution image segmentation. Secondly, it classifies grassland types and evaluates grassland degradation degree through high resolution characteristics of Landsat 8 image. Thirdly, it obtained grass yield model and quality classification through high resolution and wide scanning width characteristics of MODIS images and sample investigate data. Finally, it performs grassland field qualitative analysis through UAV remote sensing image. According to project area implementation, it proves that multi-source remote sensing data can be applied to the grassland resources investigation in Yunnan province and it is indispensable method.

scholarly journals An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration

2021 ◽  
Vol 13 (9) ◽  
pp. 1657
Author(s):  
Junyan Lu ◽  
Hongguang Jia ◽  
Tie Li ◽  
Zhuqiang Li ◽  
Jingyu Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Matching ◽  
Local Area ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Fine Grained ◽  
Feature Based ◽  
Instance Segmentation

Feature-based remote sensing image registration methods have achieved great accomplishments. However, they have faced some limitations of applicability, automation, accuracy, efficiency, and robustness for large high-resolution remote sensing image registration. To address the above issues, we propose a novel instance segmentation based registration framework specifically for large-sized high-resolution remote sensing images. First, we design an instance segmentation model based on a convolutional neural network (CNN), which can efficiently extract fine-grained instances as the deep features for local area matching. Then, a feature-based method combined with the instance segmentation results is adopted to acquire more accurate local feature matching. Finally, multi-constraints based on the instance segmentation results are introduced to work on the outlier removal. In the experiments of high-resolution remote sensing image registration, the proposal effectively copes with the circumstance of the sensed image with poor positioning accuracy. In addition, the method achieves superior accuracy and competitive robustness compared with state-of-the-art feature-based methods, while being rather efficient.

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