Journal of Geo-information Science >
Multi-source Remote Sensing Image Deep Feature Fusion Matching Algorithm
Received date: 2022-04-18
Revised date: 2022-05-22
Online published: 2023-04-19
Supported by
National Key Basic Research Strengthen Foundation of China(2020-JCJQ-ZD-015-00-03)
Focusing on the difficulty in image matching caused by different imaging mechanisms and large nonlinear spectral radiation distortion between multi-source remote sensing images, a deep Feature Fusion Matching (FFM) algorithm is proposed in this study. Firstly, the feature pyramid network is constructed to extract image deep features, and the feature connection structure is used to complementarily fuse high-level features with rich semantics and low-level features with accurate positioning, so as to solve the problem of difficult representation of homonymous features in multi-source remote sensing images and improve the positioning accuracy of feature vectors. Secondly, the feature map of the original dimension 1/8 is cross transformed to fuse its own neighborhood information and the feature information of the image to be matched. The first matching result is obtained by calculating the similarity score between the feature vectors. For the sparse feature area, an adaptive score threshold detection algorithm using sliding window is proposed to improve the matching effectiveness for sparse feature regions. Then the matching results are mapped to the sub-pixel feature graph, and the expected value of the matching probability distribution between pixels is calculated in a small window to check and optimize the matching results and improve the accuracy of matching point pairs. Finally, the PROSAC algorithm is used to purify the precise matching results, which can effectively eliminate the false matching and keep the correct matching points to the maximum extent. The experiment selects six pairs of multi-source remote sensing images, and compares FFM with SuperPoint, SIFT, ContextDesc, and LoFTR algorithms. The results show that the FFM algorithm is superior to other algorithms in terms of number of correct matching point pairs, matching point accuracy, matching point root mean square error, and matching point distribution uniformity. The FFM matching results are used for multi-source remote sensing images registration, and the registration efficiency is also greatly improved.
WANG Longhao , LAN Chaozhen , YAO Fushan , HOU Huitai , WU Beibei . Multi-source Remote Sensing Image Deep Feature Fusion Matching Algorithm[J]. Journal of Geo-information Science, 2023 , 25(2) : 380 -395 . DOI: 10.12082/dqxxkx.2023.220197
算法1 滑动窗口自适应得分阈值检测 |
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1. 初始化滑动窗口 2. 窗口滑动检测 若当前窗口内所有特征得分 计算匹配稀疏节点内自适应阈值 遍历窗口内特征向量,若得分 ,加入初次匹配点集 窗口滑动 若当前窗口存在特征向量得分 ×窗口滑动 3. 窗口滑动遍历 |
表1 试验数据对比分析Tab. 1 Comparative analysis of test data |
影像组别 | ||||||
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第1组 | 第2组 | 第3组 | 第4组 | 第5组 | 第6组 | |
基准影像 类型 | 无人机 光学影像 | ZY-3 PAN 全色影像 | Google 光学影像(夏) | Google 光学影像 | Google 光学影像 | Google 光学影像 |
图幅/像素 | 1920×1080 | 1000×1000 | 960×960 | 512×512 | 256×256 | 500×500 |
分辨率/m | - | 2.5 | 0.5 | 160 | 120 | 40 |
待匹配 影像类型 | 无人机 热红外影像 | GF-3 SAR | Google 光学影像(冬) | ZY-3 PAN 全色影像 | GF-2 PAN 全色影像 | OSM 栅格地图 |
图幅/像素 | 640×512 | 1000×1000 | 640×640 | 628×531 | 400×400 | 500×500 |
分辨率/m | - | 2.5 | 0.5 | 160 | 120 | 40 |
差异 | 可见光-热红外,成像模式与波段不同,角度、尺度差异大 | 光学-SAR,成像模式不同,灰度梯度差异大[1] | 时相差异大,冬夏季地物差异明显,角度尺度差异明显 | 普通光学影像-全色影像,波段不同,灰度差异明显 | 普通光学影像-全色影像,波段不同,灰度差异明显 | 可见光-栅格地图,不同地图模式,灰度差异大[1] |
算法2 匹配点对分布均匀度计算 |
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1. 根据图10将影像划分为5个方向即10个区域 2. 分别统计每个区域内的匹配点数量 3. 将10个区域内的匹配点数量组合为区域统计分布向量 4. 利用式(11)计算匹配点分布均匀度 |
表2 匹配试验结果对比Tab. 2 Comparison of registration test results |
影像组别 | |||||||
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第1组 | 第2组 | 第3组 | 第4组 | 第5组 | 第6组 | ||
P/对 | SuperPoint | 56 | 4 | 76 | 94 | 10 | 2 |
ContextDesc | 27 | 0 | 17 | 107 | 39 | 0 | |
SIFT | 0 | 0 | 21 | 68 | 42 | 0 | |
LoFTR | 165 | 598 | 49 | 701 | 59 | 84 | |
FFM | 321 | 416 | 246 | 267 | 165 | 30 | |
MA/% | SuperPoint | 13.1 | 1.9 | 22.2 | 40.5 | 34.48 | 3.17 |
ContextDesc | 39 | 0 | 23.9 | 71.8 | 62.9 | 0 | |
SIFT | 0 | 0 | 6.1 | 20.0 | 45.65 | 0 | |
LoFTR | 21.02 | 53.35 | 22.48 | 70.67 | 32.96 | 92.3 | |
FFM | 94.1 | 71.6 | 54.91 | 63.4 | 93.2 | 23.8 | |
RMSE | SuperPoint | 5.3140 | 38.8776 | 3.3387 | 4.3474 | 10.4386 | - |
ContextDesc | 16.2799 | - | 8.3289 | 4.6065 | 10.729 | - | |
SIFT | - | - | 7.8878 | 4.551 | 8.53 | - | |
LoFTR | 7.4840 | 5.9680 | 4.3220 | 3.0455 | 4.85276 | 3.2743 | |
FFM | 2.9675 | 3.3287 | 1.3759 | 2.87 | 3.89 | 3.1744 | |
t | SuperPoint | 1.0 | 1.2 | 0.7 | 0.5 | 0.43 | 0.43 |
ContextDesc | 5.9 | 5.1 | 3.6 | 3.2 | 2 | 2.6 | |
SIFT | 5.1 | 5.1 | 3.1 | 3.2 | 1.5 | 2.5 | |
LoFTR | 1.3 | 1.5 | 1.1 | 1.0 | 0.8 | 0.9 | |
FFM | 1.4 | 1.7 | 1.2 | 1.5 | 1.1 | 1.1 |
注:表中加粗数值表示本文FFM算法所得结果。 |
表3 匹配点分布均匀度对比Tab. 3 Matching point distribution uniformity comparison |
影像组别 | 1 | 2 | 3 | 4 | 5 | 6 |
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LoFTR | -10.4480 | -10.3252 | -9.4438 | -10.2546 | -9.7833 | -7.9718 |
FFM | -9.7832 | -9.1421 | -8.3388 | -7.5459 | -8.2412 | -3.485 |
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