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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

共 707 条记录，以下是81-90 订阅

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Side-slither Radiometric Calibration Mode Design and In-orbit Verification of Ziyuan-1(02D) Satellite Hyperspectral imager

Side-slither Radiometric Calibration Mode Design and In-orbi...

引用

2022 International conference on image, Signal processing, and pattern recognition, ISPP 2022

作者： Liang, Deyin Yu, Jing Han, Bo Zhu, Haijian Institute of Remote Sensing Satellite CAST Beijing100094 China Shanghai Institute of Technical physics Shanghai200083 China

ISBN: (纸本)9781510654846

The radiometric calibration accuracy of a hyperspectral imager is a key link in its quantitative application. The side-slither radiometric calibration can realize the high-frequency and full field of view relative radiometric correction of the hyperspectral imager, which lays the foundation for the subsequent quantitative application of hyperspectral data. In this paper, a hyperspectral imager relative radiometric calibration method and data processing method based on satellite platform 90° yaw maneuver is proposed. A verification of this method is carried out based on the in-orbit data of Ziyuan-1(02D) satellite. The results show that the proposed method has good performance for relative radiometric calibration. The relative radiometric calibration accuracy of all the sensor pixels can reach 1%, and the fringe noise in the original image is well removed. © 2022 SPIE.

关键词： Satellites

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Rotated Object Detection of High Resolution remote sensing image Based on Yolov5 5

Rotated Object Detection of High Resolution Remote Sensing I...

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5th International conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Yang, Chen Mengxing, Huang Jinjin, Ye Siling, Feng Yuanyuan, Wu Hainan University College of Information and Communication Engineering Haikou China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Nowadays, object detection is an increasingly important technology in the field of remote sensing image processing, which is applied to locate and identify high value ground objects in high resolution remote sensing images. High resolution remote sensing images have many kinds of objects, some small objects are difficult to be detected because there are too many objects. Based on some existing technologies, this paper proposes a novel algorithm for rotated object detection problem of remote sensing images. The angle is added for detection on the original yolov5 algorithm, which is also introduce mosaic-9 to enhance the dataset and expand the number of samples. Combined with the circular smooth label technology as an independent module, it is added to the training and loss of the network to solve the discontinuous at the boundary for object detection. The detection performance of the proposed model is evaluated on the public dataset DOTA, which shows the modified yolov5 algorithm reliability by this experiment. © 2022 IEEE.

关键词： Object detection

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EGF: An Improved Edge Detection Model for Low-Resolution images 2

EGF: An Improved Edge Detection Model for Low-Resolution Ima...

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2nd IEEE International conference on Futuristic Technologies, INCOFT 2023

作者： Deepak Raj, D.M. Shanmuganathan, Harinee Geetha, A. Keerthika, V. Alliance University Department of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350308846

Edge detection can benefit many different industries and domains, including computer vision, machine learning, image analysis, remote sensing, thermal imaging, pattern recognition, and medical imaging. The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in a picture serve as the object's limits and can reveal crucial details about the object's size, shape, and position. Since low-resolution images have low pixel densities or pixel values, which muddy the images, detecting edges in them is demanding work. This paper proposes a novel edge-detection approach called EGF (Extended Gaussian Filter) for low-resolution images. EGF utilizes the basic concept of Gaussian filter to find the edges of images. The objective function of EGF is developed to reduce the noise and pixel differentiation in images. The outcomes show that the suggested strategy outperforms the conventional edge detection technique. © 2023 IEEE.

关键词： edge detection gaussian filter image processing machine learning pre-processing

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remote sensing Road Segmentation Based on Feature Extraction Optimization and Skeleton Detection Optimization 7th

Remote Sensing Road Segmentation Based on Feature Extraction...

引用

7th China High-resolution Earth Observation conference, CHREOC 2020

作者： Dong, Sijun Zhao, Yu Chen, Zhengchao Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of Chinese Acdemy of Sciences Beijing100049 China

ISBN: (纸本)9789811657344

High-resolution remote sensing image segmentation is a mature application in many industrial-level image applications, such as those in the military, civil, and other fields. Scene analysis needs to be automated in high-resolution remote sensing images as much as possible. Nowadays, with the rise of deep learning algorithms, remote sensing image processing algorithms have made tremendous progress. Deep learning algorithms process unlabeled data by learning a certain amount of labeled data. We conducted a specific study on the road target with GF1 data collected in China, and the remote sensing image’s resolution was 2 m (Jin et al. in J Anhui Agri Sci 43:358–362, 2015 [Jin et al. in J Anhui Agri Sci 43:358–362, 2015]). According to the observation of road features in remote sensing images, it still has a large number of small roads that are difficult to distinguish in the 2 m resolution GF1 remote sensing image. Due to the limitations of the downsampling calculation of the fully convolutional neural network, it is easy to lose a lot of information on small roads (Long et al. in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440, 2015 [Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp 3431–3440]). Therefore, we have adjusted the feature extraction and backbone networ. We adopted EfficientNet (Tan and Le in Efficientnet: Rethinking model scaling for convolutional neural networks, 2019 [Tan M, Le QV (2019) Efficientnet: rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946]) as the skeleton network of the algorithm, and combined D-linkNet (Zhou et al. in CVPR workshops, pp. 182–186, 2018 [Zhou L, Zhang C, Wu M (2018) D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: CVPR workshops,

关键词： Feature extraction

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Automated image processing for remote sensing Data Classification 26th

Automated Image Processing for Remote Sensing Data Classifi...

引用

26th International conference on pattern recognition, ICPR 2022

作者： Reggiannini, Marco Papini, Oscar Pieri, Gabriele Institute of Information Science and Technologies National Research Council of Italy Via G. Moruzzi 1 Pisa56124 Italy

ISBN: (纸本)9783031377419

remote sensing technologies allow for continuous and valuable monitoring of the Earth’s various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula. © 2023, Springer Nature Switzerland AG.

关键词： Surface temperature

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AUTOMATIC ENRICHMENT OF INDOOR 3D MODELS USING A DEEP LEARNING APPROACH BASED ON SINGLE imageS WITH UNKNOWN CAMERA POSES

AUTOMATIC ENRICHMENT OF INDOOR 3D MODELS USING A DEEP LEARNI...

引用

ISPRS WG V/7 conference on Measurement, Visualisation and processing in BIM for Design and Construction Management ii

作者： Jarzabek-Rychard, M. Maas, H-G Wroclaw Univ Environm & Life Sci Inst Geodesy & Geoinformat Wroclaw Poland Tech Univ Dresden Inst Photogrammetry & Remote Sensing Dresden Germany

3D building modeling is a diverse field of research with a multitude of challenges, where data integration is an inherent component. The intensively growing market of BIM-related consumer applications requires methods and algorithms that enable efficient updates of existing 3D models without the need for cost-intensive data capturing and repetitive reconstruction processes. We propose a novel approach for semantic enrichment of existing indoor models by window objects, based on amateur camera RGB images with unknown exterior orientation parameters. The core idea of the approach is the parallel estimation of image camera poses with semantic recognition of target objects and their automatic mapping onto a 3D vector model. The presented solution goes beyond pure texture matching and links deep learning detection techniques with camera pose estimation and 3D reconstruction. To evaluate the performance of our procedure, we compare the estimated camera parameters with reference data, obtaining median values of 13.8 cm for the camera position and 1.1 degrees for its orientation. Furthermore, a quality of 3D mapping is assessed based on the comparison to the reference 3D point cloud. All the windows presented in the data source were detected successfully, with a mean distance between both point sets equal to 3.6 cm. The experimental results prove that the presented approach achieves accurate integration of objects extracted from single images with an input 3D model, allowing for an effective increase of its semantic coverage.

关键词： Building Information Model (BIM) deep learning object recognition texture mapping camera pose estimation

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UAV Visual Localization Technology Based on Heterogenous remote sensing image Matching 5

UAV Visual Localization Technology Based on Heterogenous Rem...

引用

5th International conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Tang, Haoyang Shi, Jiakun Miao, Xin Wu, Ruichen Yang, Dongfang School of Automation Xi'an University of Posts and Telecommunications Xi'an710121 China School of Computer Science Xi'an University of Posts and Telecommunications Xi'an710121 China The Rocket Force University of Engineering Graduate School Xi'an710025 China

ISBN: (纸本)9781450396899

At present, the positioning function of intelligent UAVs mainly uses GPS technology, and GPS signals are susceptible to environmental and electromagnetic interference factors. In this paper, we combine remote sensing image processing with image matching algorithms to propose a GPS-independent visual localization technique for UAVs. First, the VGG16 network is used as the feature extraction backbone network, and the backbone network is designed and optimized for the characteristics of heterogenous remote sensing images. Secondly, a feature point screening and matching strategy is constructed, by which common feature points between heterogeneous remote sensing images can be screened and used for feature matching. Finally, the remote sensing image containing geographic location information and the UAV aerial image are fed into the network for feature extraction and matching, and the transformation matrix between the aligned images is calculated by the successfully matched feature points, and the transformation matrix is used to complete the mapping from the aerial image to the satellite image, and finally the geographic location information of each pixel can be read from the mapped image to complete the localization. © 2022 ACM.

关键词： Unmanned aerial vehicles (UAV)

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remote sensing image Super-Resolution via Efficient Non-Local Feature Extraction Strategy 6

Remote Sensing Image Super-Resolution via Efficient Non-Loca...

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6th International conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Shi, Jinpeng Liang, Dong Weng, Shizhuang School of Electronic and Information Engineering Anhui University China

ISBN: (纸本)9798400707674

remote sensing (RS) images typically exhibit com plex spatial distributions, making non-local features critical for achieving high-quality super-resolution (SR). Most existing SR networks extract local and non-local features alternately, making the non-local feature to be explored at high spatial resolution. This leads to substantial computational costs and limits the performance of these networks. In this paper, we propose an efficient non-local feature extraction strategy to solve this problem. Specifically, we propose a dual branch super-resolution network (DBSRN) with different branches focusing on local and non-local feature extraction. For the local feature extraction branch (LFEBranch), we design an adaptive feature enhancement block (AFEB) to optimize its processing of local features. For the non-local feature extraction branch (NFEBranch), we propose a non-local feature aggregation block (NFAB) to extract non-local features more efficiently by continuously reducing the spatial resolution of the input. Extensive experiments have demonstrated that the proposed DBSRN can effectively leverage the non-local features of RS images, resulting in superior SR performance compared to state-of-the-art networks. © 2023 ACM.

关键词： Extraction

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Study on the Spatial pattern of Small Towns Based on Computer image processing and image recognition - Taking Guanhaiwei Town of Cixi as an Example

Study on the Spatial Pattern of Small Towns Based on Compute...

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2023 International conference on remote sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Yu, Qiqi School of Tourism and Urban-Rural Planning Zhejiang Gongshang University Hangzhou310018 China

ISBN: (纸本)9781510668898

This paper uses GIS and Fragstats4.2 software to quantitatively analyze the landscape space of Guanhaiwei Town, and visualize the data combined with image processing technology to enhance its spatial expression, analyze the hydrological characteristics of river corridors in Guanhaiwei Town, and the accessibility of traffic corridors, etc., and summarize the current problems. It is found that the construction land patches are concentrated on the traffic arteries and there are construction land disturbances within the woodland patches;the construction land is widely distributed, with scattered scattered points and irregular shapes. The woodland is mainly distributed in the south of the town, and the green patches in the town are less and the layout of green space is not reasonable enough, and the articulation with the surrounding landscape is weak. In the traffic land, the patches are highly fragmented and the landscape area is small. The patches of agricultural land are the landscape matrix, but their edges are highly fragmented, with small patches distributed sporadically and irregular in shape. The water patches were highly fragmented and no isolation zone was established. The study used GIS for image processing and spatial analysis, which fully reflects the importance and application of computer technology in geographic research. © 2023 SPIE.

关键词： Geographic information systems

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Estimation of Varietal Classification and Quality Analysis in Rice Seeds Using Digital image processing - A Survey

Estimation of Varietal Classification and Quality Analysis i...

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2023 IEEE International conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Sandhiya, M. Christy, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350305708

Agriculture plays a major role in today's world. 70% of rice production is cultivated in south India. Thailand, the US, India, Vietnam, etc., are exporting rice all over the world. But they are facing one of the most difficult tasks: assuring the rice seed quality. Good seeds can increase production, so selecting seeds is very important. A maximum of the work was done physically or by using huge, expensive machines. For choosing good seeds, agronomists be conscious of two factors, such as the variety which is determined by some quality criteria, and next by the skilled one who is going to analyze the rice seed physically. Both tasks take a long time and the result of accuracy is also not as expected. Nowadays computer vision technology with Digital image processing plays a major role in many fields, namely agronomy, healthcare, machine vision, pattern recognition, remote sensing, Video processing, etc. Our research work mainly focuses on the agricultural arena, going to provide good quality seeds and identify dissimilar varieties of rice seeds by digital image processing techniques such as image capturing, segmenting the rice seeds, extracting the features from the rice seeds and finally feeding into the machine learning algorithm to find out the accuracy too. © 2023 IEEE.

关键词： Classification of rice seeds Machine learning algorithm Machine vision Quality analysis of rice seed Rice seed varietal purity

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