Computational topology has consequently shorten the time taken for imagerecognition with good accuracy and therefore has boosted the performance of computer vision. This paper uses computational topology in different...
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With its unique advantages of high flexibility and high efficiency, UAV has become a reasonable substitute for conventional aerial measurement technology. Especially in the low altitude remotesensingimageprocessing...
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With its unique advantages of high flexibility and high efficiency, UAV has become a reasonable substitute for conventional aerial measurement technology. Especially in the low altitude remotesensingimageprocessing, the ortho-rectification and mosaic of aerial images are the key to vision-based UAV orthoimage generation. Therefore, how to select the appropriate methods to rectify and mosaic the aerial images of UAV is significance to research the automatic generation of digital orthoimages. Unfortunately, most of the existing reviews only focus on the general image mosaic techniques, and there are few special reports on the application of UAV orthoimage generation for reference. This paper presents a comprehensive survey on UAV orthoimage generation technologies. We conclude three mainstream frameworks of visual orthoimage generation, which are 2D mosaic framework based, SfM framework based and SLAM framework based methods. According to the above three specific frameworks, we first carried out a detailed description and comparative analysis of related important algorithms, and sorted out the differences, common points and inherent relationships. Considering the wide application of deep learning in UAV remotesensing, we propose some hypotheses on how to introduce deep learning technology into above three orthoimage generation frameworks. After analysis, we provide a more detailed performance quantification comparison of the two most recent potential frameworks (State-of-the-art methods based on SfM and SLAM). It is worth noting that we integrated different test data sources of UVA aerial video sequence with a general SLAM testing platform, and solve the issue that SLAM-based orthoimage generation methods are difficult to evaluate cross-platform. Finally, challenges about visual UAV orthoimage generation and future directions in addressing these challenges are also pointed out.
With the development of networks, many fields now demand higher quality in specific image areas, such as main characters in photos, lesion areas in medical images, and features in remotesensing. At the same time, the...
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Multitemporal hyperspectral image (HSI) change detection (CD) is a prevalent topic in remotesensing im-age processing. HSI CD usually consists of change feature extraction and classification. Although the high dimens...
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Multitemporal hyperspectral image (HSI) change detection (CD) is a prevalent topic in remotesensing im-age processing. HSI CD usually consists of change feature extraction and classification. Although the high dimensionality of HSIs provides rich spectral information, HSIs are prone to spectral-spatial variability that degrades change detection accuracy. Recently, tensor decomposition has been successfully applied to CD. However, there is still room for improvement. We propose a tensor ring-based CD model with alter-native change masks (TRACM-CD) for multitemporal HSIs. TRACM-CD extracts temporal change features using TR decomposition applied to different temporal change vectors. The alternative change masks con-strain the temporal change representation and guarantee the temporal symmetry for change features to facilitate recognition of background and changes. Experimental results on four real-world multitemporal HSI datasets confirm the effectiveness and superiority of TR-based CD. The proposed model outperforms its tensor counterparts and classic approaches for multitemporal HSIs.(c) 2022 Elsevier B.V. All rights reserved.
The proceedings contain 52 papers. The topics discussed include: improvement of remotesensingimage target detection algorithm based on YOLO V5;A Study of Chan-Vese model with the introduction of edge information;rea...
The proceedings contain 52 papers. The topics discussed include: improvement of remotesensingimage target detection algorithm based on YOLO V5;A Study of Chan-Vese model with the introduction of edge information;real-time monitoring algorithm of muscle state based on sEMG signal;lane detection network with direction context;anomaly pixel detection via dual-branch uncertainty metrics;high precision license plate recognition algorithm in open scene;implementation and design of metro process quality inspection system based on imageprocessing technology;the research on remotesensingimage change detection based on deep learning;research on aircraft wheel hub pose detection method based on machine vision;lunar dome detection method based on few-shot object detection;and image enhancement algorithm of foggy sky with sky based on sky segmentation.
Accurate building footprint extraction from optical remotesensingimages remains challenging due to the diverse appearance and complex scenarios. Although recent deep learning-based methods have been shown to greatly...
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To address the multi-scale problem and interference of differences between data in remotesensingimage segmentation, a multi-scale Siamese dual decoding network is proposed. The twin network is used as the backbone n...
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Nonuniform haze on remotesensingimages degrades image quality and hinders many high-level tasks. In this paper, we propose a Nonuniformly Dehaze Network towards nonuniform haze on visible remotesensingimages. To e...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
Nonuniform haze on remotesensingimages degrades image quality and hinders many high-level tasks. In this paper, we propose a Nonuniformly Dehaze Network towards nonuniform haze on visible remotesensingimages. To extract robust haze-aware features, we propose Nonuniformly Excite (NE) module. Inspired by the well-known gather-excite attention module, NE module works in a map-excite manner. In the map operation, we utilize a proposed Dual Attention Dehaze block to extract local enhanced features. In the gather operation, we utilize a strided deformable convolution to nonuniformly process features and extract nonlocal haze-aware features. In the excite operation, we employ a pixel-wise attention between local enhanced features and nonlocal haze-aware features, to gain finer haze-aware features. Moreover, we recursively embed NE modules in a multi-scale framework. It helps not only significantly reduce network's parameters, but also recursively deliver and fuse haze-aware features from higher levels, which makes learning more efficient. Experiments demonstrate that the proposed network performs favorably against the state-of-the-art methods on both synthetic and real-world images.
Unmanned aerial vehicle remotesensingimages suffer from problems such as arbitrary object orientation and dense arrangement of small targets, which makes horizontal box object detection difficult. To address these i...
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Landslide as a natural disaster, it is important to obtain accurate information of landslide spatial distribution. The current method of landslide extraction is mainly based on the spectral features, spatial structure...
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