Remote Sensing Image Registration Using Convolutional Neural Network Features

Ye, Famao; Su, Yongxuan; Xiao, Hui; Zhao, Xuqing; Min, Weidong

doi:10.1109/lgrs.2017.2781741

Cited by 115 publications

(58 citation statements)

References 19 publications

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“…The removal of boresight misalignments is largely affected by the accuracy of tie points. Thanks to the progress of image matching techniques [ 49 , 50 , 51 , 52 , 53 , 54 ], automatic tie points extraction with high accuracy becomes operational.…”

Section: Methodsmentioning

confidence: 99%

Direct Georeferencing for the Images in an Airborne LiDAR System by Automatic Boresight Misalignments Calibration

Liu

et al. 2020

Sensors

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Airborne Light Detection and Ranging (LiDAR) system and digital camera are usually integrated on a flight platform to obtain multi-source data. However, the photogrammetric system calibration is often independent of the LiDAR system and performed by the aerial triangulation method, which needs a test field with ground control points. In this paper, we present a method for the direct georeferencing of images collected by a digital camera integrated in an airborne LiDAR system by automatic boresight misalignments calibration with the auxiliary of point cloud. The method firstly uses an image matching to generate a tie point set. Space intersection is then performed to obtain the corresponding object coordinate values of the tie points, while the elevation calculated from the space intersection is replaced by the value from the LiDAR data, resulting in a new object point called Virtual Control Point (VCP). Because boresight misalignments exist, a distance between the tie point and the image point of VCP can be found by collinear equations in that image from which the tie point is selected. An iteration process is performed to minimize the distance with boresight corrections in each epoch, and it stops when the distance is smaller than a predefined threshold or the total number of epochs is reached. Two datasets from real projects were used to validate the proposed method and the experimental results show the effectiveness of the method by being evaluated both quantitatively and visually.

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Section: Methodsmentioning

confidence: 99%

Direct Georeferencing for the Images in an Airborne LiDAR System by Automatic Boresight Misalignments Calibration

Liu

et al. 2020

Sensors

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“…It has been widely used in carriers, such as robots. In [42][43][44][45][46][47][48], the latest image feature extraction and image retrieval technologies were discussed, along with an analysis of the state-of-the-art methods for image location recognition, using deep learning and visual positioning based on traditional image features. It was concluded that the positioning success rate of neural network models based on deep-learning training needs to be improved and that it is difficult for the positioning accuracy to reach the decimeter level.…”

Section: Related Workmentioning

confidence: 99%

A Precise Indoor Visual Positioning Approach Using a Built Image Feature Database and Single User Image from Smartphone Cameras

Chen

Liao

et al. 2020

Remote Sensing

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Indoor visual positioning is a key technology in a variety of indoor location services and applications. The particular spatial structures and environments of indoor spaces is a challenging scene for visual positioning. To address the existing problems of low positioning accuracy and low robustness, this paper proposes a precision single-image-based indoor visual positioning method for a smartphone. The proposed method includes three procedures: First, color sequence images of the indoor environment are collected in an experimental room, from which an indoor precise-positioning-feature database is produced, using a classic speed-up robust features (SURF) point matching strategy and the multi-image spatial forward intersection. Then, the relationships between the smartphone positioning image SURF feature points and object 3D points are obtained by an efficient similarity feature description retrieval method, in which a more reliable and correct matching point pair set is obtained, using a novel matching error elimination technology based on Hough transform voting. Finally, efficient perspective-n-point (EPnP) and bundle adjustment (BA) methods are used to calculate the intrinsic and extrinsic parameters of the positioning image, and the location of the smartphone is obtained as a result. Compared with the ground truth, results of the experiments indicate that the proposed approach can be used for indoor positioning, with an accuracy of approximately 10 cm. In addition, experiments show that the proposed method is more robust and efficient than the baseline method in a real scene. In the case where sufficient indoor textures are present, it has the potential to become a low-cost, precise, and highly available indoor positioning technology.

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“…Combinations of hand-crafted features and deep learning features are proposed to absorb their respective advantages. Ye et al [12] demonstrated that the SIFT feature loses much middle-or high-level information, and proposed to fuse SIFT and deep convolutional neural network (CNN) features for remote sensing image registration. Reyes et al [13] trained the conditional generative adversarial networks (cGANs) to generate SAR-like image patches from optical images, and used hand-crafted features to match the artificially generated patches.…”

Section: Introductionmentioning

confidence: 99%

Automatic Registration of Optical and SAR Images Via Improved Phase Congruency Model

Xiang

Tao

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we propose an automatic and efficient method to solve optical and SAR image registration using the improved phase congruency (PC) model. First, evenly-distributed keypoints are extracted from the optical images via the block harris method. Complementary grid points are then selected in image regions with poor structural information and supplemented to the keypoint set. For each keypoint, a robust feature representation that captures the local spatial relationship is proposed based on the improved PC model. Specifically, we propose to use two different PC models, the classic PC and the SAR-PC, to construct features for optical and SAR images respectively. The PC features of several directions are aggregated to construct the feature descriptors, and a similarity metric via the phase correlation of feature descriptors is obtained. The proposed similarity metric can not only find accurate correspondence but also present efficient results without presetting the size of the search region. We compare the proposed method with two baselines and stateof-the-art (SOTA) methods, i.e. OS-SIFT, HOPC, CFOG, in various scenarios. The results show that the proposed method outperforms the baselines and shows comparable performance with SOTA methods in regions with abundant structural information and better performance in regions with less structural information. Moreover, we build a high-resolution optical and SAR image matching dataset, which consists of 10,692 nonoverlapping patch-pairs of 256 × 256 pixels and 1-m resolution. Results of two benchmarks, Siamese deep matching network and conditional Generative Adversarial Networks, show that this dataset is practical and challenging.

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Remote Sensing Image Registration Using Convolutional Neural Network Features

Cited by 115 publications

References 19 publications

Direct Georeferencing for the Images in an Airborne LiDAR System by Automatic Boresight Misalignments Calibration

Direct Georeferencing for the Images in an Airborne LiDAR System by Automatic Boresight Misalignments Calibration

A Precise Indoor Visual Positioning Approach Using a Built Image Feature Database and Single User Image from Smartphone Cameras

Automatic Registration of Optical and SAR Images Via Improved Phase Congruency Model

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