- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Generating a hyperspectral digital surface model using a hyperspectral 2D frame camera
摘要： Miniaturised 2D frame format hyperspectral camera technology that is suitable for small unmanned aerial vehicles (UAVs) has entered the market, making the generation of hyperspectral digital surface models (HDSMs) feasible. HDSMs offer a rigorous approach to capturing the target spectral and 3D geometric data. The main objective of this investigation was to study and develop techniques for the generation of HDSMs in forest areas using novel hyperspectral 2D frame camera technologies. An approach based on object-space image matching was developed, adapting the traditional vertical line locus (VLL) method for HDSM generation; this was then named the hyperspectral VLL (HVLL) approach. Additionally, image classification was introduced into the processing chain in order to adapt the matching parameters, based on different classes. We also proposed a method for extracting the spectral and viewing angle information of the points. An empirical study was carried out using UAV datasets from tropical and boreal forests using 2D format hyperspectral cameras, based on tuneable Fabry-Pérot interferometer (FPI) technology. Quality assessment was performed using DSMs based on state-of-the-art commercial software and airborne laser scanning (ALS). The results showed that the proposed technique generated a high-quality HDSM in both tested environments. The HDSM had higher deviations over the continuous canopy cover than the digital surface models (DSMs) generated using commercial software. The method using image classification information outperformed the commercial approach with respect to the ability to measure ground points in shadowed areas and in canopy gaps. The proposed method is of great interest in supporting automated interpretations of novel multi- and hyperspectral imaging technologies, especially when applied complex objects, such as forests.
关键词： Forest,Hyperspectral 2D frame camera,Image matching,Hyperspectral digital surface model
更新于2025-09-23 15:23:52
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Research on feature point extraction and matching machine learning method based on light field imaging
摘要： At present, there are many methods to realize the matching of specified images with features, and the basic components include image feature point detection, feature description, and image matching. Based on this background, this article has done different research and exploration around these three aspects. The image feature point detection method is firstly studied, which commonly include image edge information-based feature detection method, corner information-based detection method, and various interest operators. However, all of the traditional detection methods are involved in problems of large computation burden and time consumption. In order to solve this problem, a feature detection method based on image grayscale information-FAST operator is used in this paper, which is combined with decision tree theory to effectively improve the speed of extracting image feature points. Then, the feature point description method BRIEF operator is studied, which is a local expression of detected image feature points based on descriptors. Since the descriptor does not have rotation invariance, the detection operator is endowed by a direction that is proposed in this paper, and then the local feature description is conducted on the feature descriptor to generate a binary string array containing direction information. Finally, the feature matching machine learning method is analyzed, and the nearest search method is used to find the nearest feature point pair in Euclidean distance, of which the calculation burden is small. The simulation results show that the proposed nearest neighbor search and matching machine learning algorithm has higher matching accuracy and faster calculation speed compared with the classical feature matching algorithm, which has great advantages in processing a large number of array images captured by the light field camera.
关键词： Nearest neighbor search,Light field imaging,Image matching,Machine learning
更新于2025-09-23 15:23:52
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Cross-Domain Image Matching with Deep Feature Maps
摘要： We investigate the problem of automatically determining what type of shoe left an impression found at a crime scene. This recognition problem is made difficult by the variability in types of crime scene evidence (ranging from traces of dust or oil on hard surfaces to impressions made in soil) and the lack of comprehensive databases of shoe outsole tread patterns. We find that mid-level features extracted by pre-trained convolutional neural nets are surprisingly effective descriptors for this specialized domains. However, the choice of similarity measure for matching exemplars to a query image is essential to good performance. For matching multi-channel deep features, we propose the use of multi-channel normalized cross-correlation and analyze its effectiveness. Our proposed metric significantly improves performance in matching crime scene shoeprints to laboratory test impressions. We also show its effectiveness in other cross-domain image retrieval problems: matching facade images to segmentation labels and aerial photos to map images. Finally, we introduce a discriminatively trained variant and fine-tune our system through our proposed metric, obtaining state-of-the-art performance.
关键词： Cross-domain image matching,Similarity metric,Normalized cross-correlation
更新于2025-09-23 15:23:52
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A Novel Neural Network for Remote Sensing Image Matching
摘要： Rapid development of remote sensing (RS) imaging technology makes the acquired images have larger size, higher resolution, and more complex structure, which goes beyond the reach of classical hand-crafted feature-based matching. In this paper, we propose a feature learning approach based on two-branch networks to transform the image matching task into a two-class classification problem. To match two key points, two image patches centered at the key points are entered into the proposed network. The network aims to learn discriminative feature representations for patch matching, so that more matching pairs can be obtained on the premise of maintaining higher subpixel matching accuracy. The proposed network adopts a two-stage training mode to deal with the complex characteristics of RS images. An adaptive sample selection strategy is proposed to determine the size of each patch by the scale of its central key point. Thus, each patch can preserve the texture structure around its key point rather than all patches have a predetermined size. In the matching prediction stage, two strategies, namely, superpixel-based sample graded strategy and superpixel-based ordered spatial matching, are designed to improve the matching efficiency and matching accuracy, respectively. The experimental results and theoretical analysis demonstrate the feasibility, robustness, and effectiveness of the proposed method.
关键词： neural network,image matching,remote sensing (RS) image,Deep learning (DL)
更新于2025-09-23 15:22:29
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Helly hypergraph based matching framework using deterministic sampling techniques for spatially improved point feature based image matching
摘要： Hypergraphs are tools for matching of point-features incorporating spatial relationships in the form of hyperedges exhibiting topological and geometric features between the points of images to be matched. Considering all possible hyperedges is computationally expensive and are randomly chosen in the state of the art techniques. A Helly Hypergraph based Matching Framework (HHMF) is proposed for the matching of images using point-features with effective hyperedges. The framework includes proposed algorithms such as Construction of Hyperedges using Point-features by Random (CHPR), Combinatorial (CHPC), and Exhaustive (CHPE) sampling techniques with and without Helly selection. The resultant hyperedges are treated with Adaptive Block Co-ordinate Ascent Graph Matching with Integer Projected Fixed Point algorithm. The performance of the proposed framework is evaluated in terms of Accuracy, Matching score, Execution time and Tensor Size for synthetic point sets and Willow wine image dataset. Based on the experimental studies carried out against existing framework, CHPC, and CHPE with Helly selection, exhibited better performance with 73.88% & 81% accuracy for 53.64 & 14.8% reduced tensor size respectively, in deformation noise tests, and 98% & 96% accuracy for 97% & 70% reduced tensor size in outlier tests. In the implicit experimental comparisons within sampling techniques, CHPR, and CHPE provided better performance with 81.37%, and 76% accuracy. In general, HHMF framework has reduced the tensor size and execution time for deterministic sampling cases during point sets matching. The framework can be extended in the near future by incorporating learning schemes for automated hypergraph based point sets matching.
关键词： Image matching,Hypergraph matching,Point correspondence,Helly property
更新于2025-09-23 15:21:01
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Robust visible-infrared image matching by exploiting dominant edge orientations
摘要： Finding the correspondences between visible and infrared images is a challenging task due to the image spectral inconsistency which leads to large differences of gradient distributions between these images. To alleviate this problem, we propose a novel feature descriptor for visible and infrared image matching based on Log-Gabor filters. The descriptor employs multi-orientation and multi-scale Log-Gabor filters to encode the edge information statistically. Furthermore, the descriptor provides rotation invariance by estimating the dominant orientation which is based on accumulated edge orientations. The experimental results demonstrate the effectiveness of the proposed rotation invariant descriptor and the better performance for matching visible and longwave infrared images as compared with state-of-the-art descriptors.
关键词： Log-Gabor filters,rotation invariant descriptor,edge orientations,visible-infrared image matching
更新于2025-09-23 15:21:01
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Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data
摘要： Detecting topographic changes in an urban environment and keeping city-level point clouds up-to-date are important tasks for urban planning and monitoring. In practice, remote sensing data are often available only in different modalities for two epochs. Change detection between airborne laser scanning data and photogrammetric data is challenging due to the multi-modality of the input data and dense matching errors. This paper proposes a method to detect building changes between multimodal acquisitions. The multimodal inputs are converted and fed into a light-weighted pseudo-Siamese convolutional neural network (PSI-CNN) for change detection. Different network configurations and fusion strategies are compared. Our experiments on a large urban data set demonstrate the effectiveness of the proposed method. Our change map achieves a recall rate of 86.17%, a precision rate of 68.16%, and an F1-score of 76.13%. The comparison between Siamese architecture and feed-forward architecture brings many interesting findings and suggestions to the design of networks for multimodal data processing.
关键词： convolutional neural networks,change detection,dense image matching,airborne laser scanning,Siamese networks,multimodal data
更新于2025-09-23 15:19:57
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Region-Based Co-Seismic Ground Displacement Dectection Using Optical Aerial Imagery
摘要： Ground analysis is important after an earthquake occurred in order to understand the surface variations. By optical imagery before and after a disaster, this paper tries to find the co-seismic ground displacement using region-based image patches. By comparing the similarity of pre- and post-event orthophotos, regions of ground changes can be detected. Conjugate objects were then recognized through feature extraction and image matching. An image with pose parameters can estimate the three-dimensional positions of those objects in the world space. Thus, three-dimensional geometric correlation between the pre- and post-event objects can measure the ground displacement. The results indicate that the proposed approach is able to estimate the co-seismic ground displacements based on the spatial information from optical imagery. In addition, surface shifts can be identified based on the spatial correlations.
关键词： spatial correlation,Image similarity,ground variation,image matching
更新于2025-09-10 09:29:36
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[Lecture Notes in Electrical Engineering] Advanced Multimedia and Ubiquitous Engineering Volume 518 (MUE/FutureTech 2018) || A Scene Change Detection Framework Based on Deep Learning and Image Matching
摘要： The scene change detection (SCD) is the necessary issue for further video applications. Current SCD techniques employ the Convolutional Neural Networks (CNN) for image feature learning and outperform traditional optical ?ow methods. However, those methods use traditional machine learning methods for scene classi?cation. In the paper, we present a novel framework through deep learning networks and image matching method. We employ the ResNet for video image training and learning to classify the video frames into different categories. The classi?ed frames can be used for scene change detection. For those predicted scene changed frame pairs, we adopt the SIFT algorithm to extract features of them and through image matching to remove the failure detection. We perform the experiments on several different kinds of videos. The proposed framework can detect the scene changing parts and divide the images into scene units with low error rate.
关键词： Scene change detection,Deep learning,Image matching
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - SAR Image Matching Improvement Using Image Texture Analysis
摘要： Matching in high-resolution synthetic aperture radar (SAR) images while being more complicated compared to optical images, is especially important due to its numerous applications. The main aim of current research is to determine improvement of SAR image matching process by deploying texture analysis using gray level’s co-occurrence matrix (GLCM). Three parts of the pair of TerraSAR-X images are used to implement the methodology. The results show that for some areas with low texture, the conventional image matching algorithm is not able to detect corresponding points, while using other textural features in image matching process leads to improvement in quantity of acceptable matched points.
关键词： GLCM,textural feature,optical flow,SAR image matching
更新于2025-09-10 09:29:36