IR remote sensing image registration based on multi-scale feature extraction

2014 ◽  
Author(s):  
Jun Kong ◽  
Min Jiang ◽  
Jun Kong ◽  
Jun Kong ◽  
Yi-Ning Sun
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Image Registration ◽  
Remote Sensing Image ◽  
Scale Feature ◽  
Multi Scale

Recognition of small targets in remote sensing image using multi-scale feature fusion-based shot multi-box detector

2021 ◽  
Vol 29 (11) ◽  
pp. 2672-2682
Author(s):  
Xin CHEN ◽  
◽  
Min-jie WAN ◽  
Chao MA ◽  
Qian CHEN ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
Scale Feature ◽  
Multi Scale ◽  
Small Targets

scholarly journals MCNet: Multi-Scale Feature Extraction and Content-Aware Reassembly Cloud Detection Model for Remote Sensing Images

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 28
Author(s):  
Ziqiang Yao ◽  
Jinlu Jia ◽  
Yurong Qian
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Complex Surface ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Scale Feature ◽  
Multi Scale ◽  
Content Aware

Cloud detection plays a vital role in remote sensing data preprocessing. Traditional cloud detection algorithms have difficulties in feature extraction and thus produce a poor detection result when processing remote sensing images with uneven cloud distribution and complex surface background. To achieve better detection results, a cloud detection method with multi-scale feature extraction and content-aware reassembly network (MCNet) is proposed. Using pyramid convolution and channel attention mechanisms to enhance the model’s feature extraction capability, MCNet can fully extract the spatial information and channel information of clouds in an image. The content-aware reassembly is used to ensure that sampling on the network can recover enough in-depth semantic information and improve the model cloud detection effect. The experimental results show that the proposed MCNet model has achieved good detection results in cloud detection tasks.

scholarly journals Remote Sensing Imagery Super Resolution Based on Adaptive Multi-Scale Feature Fusion Network

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1142
Author(s):  
Xinying Wang ◽  
Yingdan Wu ◽  
Yang Ming ◽  
Hui Lv
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Feature Fusion ◽  
Super Resolution ◽  
Convolutional Network ◽  
Remote Sensing Imagery ◽  
Resolution Image ◽  
Scale Feature ◽  
Multi Scale ◽  
Key Characteristics

Due to increasingly complex factors of image degradation, inferring high-frequency details of remote sensing imagery is more difficult compared to ordinary digital photos. This paper proposes an adaptive multi-scale feature fusion network (AMFFN) for remote sensing image super-resolution. Firstly, the features are extracted from the original low-resolution image. Then several adaptive multi-scale feature extraction (AMFE) modules, the squeeze-and-excited and adaptive gating mechanisms are adopted for feature extraction and fusion. Finally, the sub-pixel convolution method is used to reconstruct the high-resolution image. Experiments are performed on three datasets, the key characteristics, such as the number of AMFEs and the gating connection way are studied, and super-resolution of remote sensing imagery of different scale factors are qualitatively and quantitatively analyzed. The results show that our method outperforms the classic methods, such as Super-Resolution Convolutional Neural Network(SRCNN), Efficient Sub-Pixel Convolutional Network (ESPCN), and multi-scale residual CNN(MSRN).

scholarly journals Bidirectional Symmetry Network with Dual-Field Cyclic Attention for Multi-Temporal Aerial Remote Sensing Image Registration

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1863
Author(s):  
Ying Chen ◽  
Qi Zhang ◽  
Wencheng Zhang ◽  
Lei Chen
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Image Registration ◽  
Network Architecture ◽  
Feature Matching ◽  
Pearson Correlation ◽  
Remote Sensing Image ◽  
Aerial Image ◽  
Source Image ◽  
Multi Temporal

Multi-temporal remote sensing image registration is a geometric symmetry process that involves matching a source image with a target image. To improve the accuracy and enhance the robustness of the algorithm, this study proposes an end-to-end registration network—a bidirectional symmetry network based on dual-field cyclic attention for multi-temporal remote sensing image registration, which mainly improves feature extraction and feature matching. (1) We propose a feature extraction framework combining an attention module and a pre-training model, which can accurately locate important areas in images and quickly extract features. Not only is the dual receptive field module designed to enhance attention in the spatial region, a loop structure is also used to improve the network model and improve overall accuracy. (2) Matching has not only directivity but also symmetry. We design a symmetric network of two-way matching to reduce the registration deviation caused by one-way matching and use a Pearson correlation method to improve the cross-correlation matching and enhance the robustness of the matching relation. In contrast with two traditional methods and three deep learning-based algorithms, the proposed approach works well under five indicators in three public multi-temporal datasets. Notably, in the case of the Aerial Image Dataset, the accuracy of the proposed method is improved by 39.8% compared with the Two-stream Ensemble method under a PCK (Percentage of Correct Keypoints) index of 0.05. When the PCK index is 0.03, accuracy increases by 46.8%, and increases by 18.7% under a PCK index of 0.01. Additionally, when adding the innovation points in feature extraction into the basic network CNNGeo (Convolutional Neural Network Architecture for Geometric Matching), accuracy is increased by 36.7% under 0.05 PCK, 18.2% under 0.03 PCK, and 8.4% under 0.01 PCK. Meanwhile, by adding the innovation points in feature matching into CNNGeo, accuracy is improved by 16.4% under 0.05 PCK, 9.1% under 0.03 PCK, and 5.2% under 0.01 PCK. In most cases, this paper reports high registration accuracy and efficiency for multi-temporal remote sensing image registration.

scholarly journals PCAN—Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery

2021 ◽  
Vol 13 (7) ◽  
pp. 1243
Author(s):  
Wenxin Yin ◽  
Wenhui Diao ◽  
Peijin Wang ◽  
Xin Gao ◽  
Ya Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Power Plants ◽  
State Of The Art ◽  
Thermal Power ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Thermal Power Plants ◽  
Average Precision ◽  
Deep Convolutional Neural Networks ◽  
Multi Scale

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.

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