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

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3D remote Scene Reconstruction via Graph Convolution 10

3D Remote Scene Reconstruction via Graph Convolution

引用

10th International conference on Virtual Reality, ICVR 2024

作者： Li, Xing Sun, Mingyu Ou, Qiaofeng Mo, Yan Liu, Sikun Rao, Zhibo Nanchang Hangkong University School of Information Engineering Nanchang China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350364231

3D urban scene reconstruction is a significant remote sensing topic that provides enriched context on 3D spatial information. However, obtaining a watertight, lightweight, and detailed mesh object from remote sensing images is challenging due to sparse point clouds and inaccurate normals. This paper introduces a 3D mesh reconstruction framework to recover 3D scene information from pairwise remote sensing images, comprising three main stages. Firstly, our previous multi-task network (BGA-Net) is employed to predict the disparity maps from pairwise remote sensing images. Next, we utilize camera parameters and interpolation to reconstruct and fill point clouds, mitigating sparse point clouds (e.g., building edges or unsmooth areas). Finally, a graph convolution network (GCN) is adopted to recover the mesh object from the filled point clouds, reducing the dependency on precise normals. Extensive experiments demonstrate that our framework can qualitatively produce watertight, lightweight, and detailed mesh models. © 2024 IEEE.

关键词： Mesh generation

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Underwater Distorted image Restoration Algorithm Based on Compressed sensing and Centroid Method

Underwater Distorted Image Restoration Algorithm Based on Co...

引用

2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Zeng, Ke Feng, Chuncheng Gong, Wanqi Liu, Lei Bai, Fuyao Wang, Congzheng Institute of Optics and Electronics Chinese Academy of Sciences Sichuan Chengdu610209 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510666351

Imaging of static scenes under water is widely used in underwater detection technology. Aiming at the problem of severe distortion of underwater scene images caused by water surface fluctuations. An underwater distorted image restoration algorithm based on compressed sensing and centroid method is proposed. First, track the significant feature points between video frames to obtain the displacement trajectory of the feature points, then use the compressed sensing algorithm to reconstruct the motion vector field of the entire image to eliminate the periodic distortion of the image. Further, on this basis, the dense optical flow between the images is calculated to construct the centroid of the image sequence, thereby eliminating the randomness distortion that may exist in the image. The experimental results show that compared with the current mainstream restoration methods, the algorithm can ensure that the restored image has high definition while eliminating image distortion, and it performs well in some image quality evaluation indicators. © 2023 SPIE.

关键词： image reconstruction

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Prompt-RSVQA: Prompting visual context to a language model for remote sensing Visual Question Answering

Prompt-RSVQA: Prompting visual context to a language model f...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Chappuis, Christel Zermatten, Valerie Lobry, Sylvain Le Saux, Bertrand Tuia, Devis EPFL Sion Switzerland Univ Paris Cite F-75006 Paris France European Space Agcy Lab Frascati Italy

ISBN: (纸本)9781665487399

remote sensing visual question answering (RSVQA) was recently proposed with the aim of interfacing natural language and vision to ease the access of information contained in Earth Observation data for a wide audience, which is granted by simple questions in natural language. The traditional vision/language interface is an embedding obtained by fusing features from two deep models, one processing the image and another the question. Despite the success of early VQA models, it remains difficult to control the adequacy of the visual information extracted by its deep model, which should act as a context regularizing the work of the language model. We propose to extract this context information with a visual model, convert it to text and inject it, i.e. prompt it, into a language model. The language model is therefore responsible to process the question with the visual context, and extract features, which are useful to find the answer. We study the effect of prompting with respect to a black-box visual extractor and discuss the importance of training a visual model producing accurate context.

关键词： Training Visualization Natural languages Feature extraction Transformers Question answering (information retrieval) Data mining

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A Deep Neural Network Supported by Ground Verification Data for Active Fire Detection in MODIS

A Deep Neural Network Supported by Ground Verification Data ...

引用

2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Chen, Jinpeng Xie, Feifei Zheng, Chunkai College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781510666351

At present, the use of MODIS to detect fires mainly uses a series of thresholds to identify fire. However, in areas with high heterogeneity, the division of thresholds is more difficult, resulting in many false detections of fire, and cold fire are easily missed. The emergence of deep learning technology has improved the classification accuracy of images, but the surface information of hotspots cannot be determined only by visual interpretation of MODIS images, and the acquisition of high-quality fire samples further limits the detection performance of deep learning methods. In order to solve the above problems, this paper proposes a deep neural network fire detection method based on ground truth data. First, the data structure of the US ground wildfire dataset is studied, a spatiotemporal matching strategy between ground data and remote sensing images is designed, and a priori sample is constructed. library. Second, robust classification features are selected combining the fire physical properties and the spectral distribution of the dataset. Finally, a DNN (Deep Neural Network) for MODIS fire detection is constructed according to the image characteristics. Application experiments were carried out in three typical scenarios and compared with MODIS fire products. The results show that the improved method improves the temporal and spatial adaptability of fire detection, and the extraction of small fire in farming areas is better than MODIS fire products;especially in the suburbs Significant reduction of spurious fire in region extraction, false positives decreased by 19.8%. This method provides a new reference for coarse resolution satellite data to detect fires, and effectively improves the efficiency of fire detection. © 2023 SPIE.

关键词： remote sensing

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Research on Blind Restoration Algorithm of Motion Blurred remote sensing image 10th

Research on Blind Restoration Algorithm of Motion Blurred Re...

引用

10th International conference on Signal and Information processing, Network and Computers, ICSINC 2022

作者： Yang, Hanwen Liu, Yanran Luan, Li Wang, Yunsen Wang, Haoxuan Shanghai Jiao Tong University Shanghai200240 China Shandong Institute of Space Electronic Technology Shandong Yantai264670 China

ISBN: (纸本)9789811999673

The reconstruction of motion blur is a significant subject in remote sensing image processing. It has a great effect on the follow-up processes of target detection and recognition. To meet the needs of on-board intelligent processing, a new blind restoration method based on Radon transform and autocorrelation function is proposed to process remote sensing image blurred by motion. The method adopt frequency spectrum diagram and Radon transform to calculate the direction of motion blur image. Autocorrelation function is quoted to calculate the blur scale. After the point spread function is estimated, Wiener filter is applied to realize the restoration of motion blurred images. By rotating blurred image to the horizontal axis direction, the two-dimensional restoration problem is converted into one dimension. Test results indicate that the method can obtain satisfactory recovering results of remote sensing image blurred by linear motion. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog recognition

Annotating Only at Definite Pixels: A Novel Weakly Supervise...

引用

IEEE International conference on Visual Communications and image processing (VCIP)

作者： Zhu, Xun Xu, Mengqiu Wu, Ming Zhang, Chuang Zhang, Bin Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781665475921

Sea fog recognition is a challenging and significant semantic segmentation task in remote sensing images. The fully supervised learning method relies on the pixel-level label, which is labor-intensive and time-consuming. Moreover, it is impossible to accurately annotate all pixels of the sea fog region due to the limited ability of the human eye to distinguish between low clouds and sea fog. In this paper, we propose a novel approach of point-based annotation for weakly supervised semantic segmentation with the auxiliary information of International Comprehensive Ocean-Atmosphere Data Set (ICOADS) visibility data. It only needs several definite points for both foreground and background, which significantly reduces the annotation cost of manpower. We conduct extensive experiments on Himawari-8 satellite remote sensing images to demonstrate the effectiveness of our annotation method. The mean intersection over union (mIoU) and overall recognition accuracy of our annotation method reach 82.72% and 95.18%, respectively. Compared with the fully supervised learning method, the accuracy and the recognition rate of sea fog area are improved with a maximum increase of 7.69% and 9.69%, respectively.

关键词： sea fog recognition weakly supervised learning point annotation semantic segmentation remote sensing image

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MACHINE LEARNING AND FEATURE EXTRACTION FOR INDUSTRIAL SMOKE PLUMES DETECTION FROM SENTINEL-2 imageS

MACHINE LEARNING AND FEATURE EXTRACTION FOR INDUSTRIAL SMOKE...

引用

IEEE International Geoscience and remote sensing Symposium (IGARSS)

作者： Poucin, Florentin Ouerghi, Elyes Lajouanie, Simon Rodrigues, Hugo de Almeida Facciolo, Gabriele de Franchis, Carlo Hessel, Charles Kayrros SAS Paris France Univ Paris Saclay CNRS Ctr Borelli ENS Paris Saclay Paris France

ISBN: (纸本)9798350320107

The detection of smoke plumes by satellite imagery is a comprehensive research topic that can be used to better monitor activity and emissions from the energy and industrial sectors. In this study, we propose a machine learning methodology based on the extraction of relevant features from Sentinel-2 images to perform industrial smoke plume detection. This computer vision problem is modeled as an image classification task based on the presence or absence of plumes from previously identified sources. A dataset of nearly 17,000 hand-labeled images of smoke plumes for activity classification has been compiled to train and evaluate our detection models. The final Gradient Boosting model only uses the 3 RGB bands of Sentinel-2 and after a post-processing step reaches an accuracy of 95%.

关键词： Industrial plumes Sentinel-2 images Machine learning Feature extraction pattern recognition

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Feature Aggregation Network for Building Extraction from High-Resolution remote sensing images 20th

Feature Aggregation Network for Building Extraction from H...

引用

20th Pacific Rim International conference on Artificial Intelligence, PRICAI 2023

作者： Zhou, Xuan Wei, Xuefeng Institut Polytechnique de Paris Rte de Saclay Palaiseau91120 France

ISBN: (纸本)9789819970247

The rapid advancement in high-resolution satellite remote sensing data acquisition, particularly those achieving sub-meter precision, has uncovered the potential for detailed extraction of surface architectural features. However, the diversity and complexity of surface distributions frequently lead to current methods focusing exclusively on localized information of surface features. This often results in significant intra-class variability in boundary recognition and between buildings. Therefore, the task of fine-grained extraction of surface features from high-resolution satellite imagery has emerged as a critical challenge in remote sensing image processing. In this work, we propose the Feature Aggregation Network (FANet), concentrating on extracting both global and local features, thereby enabling the refined extraction of landmark buildings from high-resolution satellite remote sensing imagery. The Pyramid Vision Transformer captures these global features, which are subsequently refined by the Feature Aggregation Module and merged into a cohesive representation by the Difference Elimination Module. In addition, to ensure a comprehensive feature map, we have incorporated the Receptive Field Block and Dual Attention Module, expanding the receptive field and intensifying attention across spatial and channel dimensions. Extensive experiments on multiple datasets have validated the outstanding capability of FANet in extracting features from high-resolution satellite images. This signifies a major breakthrough in the field of remote sensing image processing. We will release our code soon. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Extraction

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Knowledge Based Classifier and pattern recognition Technique for Satellite image Analysis 3

Knowledge Based Classifier and Pattern Recognition Technique...

引用

3rd IEEE Global conference for Advancement in Technology, GCAT 2022

作者： Nimbarte, Nita Nagpure, Aniket Sanodiya, Badal Sevatkar, Harshal Balamwar, Sanjay Yeshwantrao Chavan College of Engineering Electronics & Telecommunication Department Nagpur India Vnit Campus Maharashtra Remote Sensing Application & Centre Nagpur India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

pattern recognition is quickly becoming a popular topic of image processing. It is a branch of remote sensing, and it can be useful where it is difficult to visit and analyze geographical locations such as forestry or islands, and it can also be difficult to visit areas affected by natural disasters. To do this, a system to distinguish areas such as buildings, greenery, cultivated land, land, water, and so on must be devised. Previously, research on these themes had been conducted, but it was confined to one or two remote sensor items. This work introduces a method for identifying items such as buildings, greenery, water, and land. Because the knowledge basis for this recognition is based on analysis, it is also unbound to specific types of locations. This method is useful for determining the area under civilization as well as the percentage area of a given pattern. The image classification technique uses supervised and unsupervised classification methods. The supervised classification uses a maximum likelihood classifier. The unsupervised classification uses the ISO Cluster classifier to classify images. ArcGIS PRO and ERDAS IMAGINE software are used for algorithm analysis. © 2022 IEEE.

关键词： remote sensing

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HR_BMNet: A boundary perception and multiresolution fusion network for semantic segmentation of remote sensing

HR_BMNet: A boundary perception and multiresolution fusion n...

引用

2024 International conference on image processing and Artificial Intelligence, ICIPAl 2024

作者： Chen, Yan Wang, Mengyuan Jiang, Wenxiang Kang, Menglei Wang, Xiaofeng Collaborative Innovation Centre for Computer Vision and Pattern Recognition School of Artificial Intelligence and Big Data Hefei University Hefei230601 China

ISBN: (纸本)9781510681514

The conventional approach for semantic segmentation of remote sensing imagery using encoder-decoder convolutional neural networks relies on the output of prior feature maps sequentially without considering the interactions between neighboring contextual feature maps with multiple resolutions. While the standard HRNet proposal has successfully improved multi-resolution semantic and spatial features to address the aforementioned issues, its lack of emphasis on boundary perception often results in inadequate target segmentation. Furthermore, a frequent occurrence of multiresolution contextual interaction in HRNet leads to the addition of a significant quantity of redundant information and amplifies the complexity of the model. Hence, to tackle the abovementioned issues, we propose a semantic segmentation network identified as HR-BMNet, which incorporates boundary sensitivity and multiple-resolution learning. The idea associated with standard HRNet is adopted as the foundational architecture. We extend novel boundary perception and multi-resolution fusion attention modules, integrating channel attention mechanisms. The strategy provides an ex-tensive optimization of edges and the efficient capture of crucial multi-scale features. During the feature combination stage, the boundary insights are employed to augment the semantic information, thereby mitigating the spatial details loss, enhancing the intra-class semantic consistency, and achieving superior segmentation. The efficacy of the proposed method is validated through comparison and ablation experiments conducted on the ISPRS Vaihingen and CSRSD datasets. Among the experiments conducted, the best ones attained a mean Intersection over Union (mIoU) of 72.11% on the Vaihingen dataset and 89.28% on the CSRSD dataset, respectively. © 2024 SPIE.

关键词： Semantic Segmentation

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