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

共 708 条记录，以下是221-230 订阅

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Survey of Drones for Agriculture Automation from Planting to Harvest 22

Survey of Drones for Agriculture Automation from Planting to...

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22nd IEEE International conference on Intelligent Engineering Systems (INES)

作者： Kulbacki, Marek Segen, Jakub Kniec, Wojciech Klempous, Ryszard Kluwak, Konrad Nikodem, Jan Kulbacka, Julita Serester, Andrea DIVE IN AI Wroclaw Poland Wroclaw Univ Sci & Technol Fac Elect Wroclaw Poland Wroclaw Med Univ Dept Med Biochem Wroclaw Poland Wroclaw Med Univ Dept Mol & Cellular Biol Wroclaw Poland Obuda Univ Antal Bejczy Ctr Intelligent Robot Budapest Hungary Polish Japanese Acad Informat Technol R&D Ctr Warsaw Poland

ISBN: (纸本)9781538611227

Farmers explore the capabilities for applications of Robotic Process Automation (RPA) with image processing, pattern recognition and machine learning, so its logical to ask where best to apply this technology for maximum effect. Machines can do automated tasks better, cheaper and faster. One can use the camera from the sky and measure things on the earth especially on cropland from drones and satellites. Over time, drones have increased in capabilities and fallen in cost, and their use has greatly expanded especially in complex terrain. High quality remote sensing with spectral imaging using drones makes them interesting for regular use in Precision Agriculture (PA). Drones are often used in agriculture in ways that were highly controversial only a short time ago even there are no unified legislation on drones usage in agriculture. In this paper we address problem of remote sensing (RS) technologies combined with Unmanned Aerial System (UAS) platforms to support and develop selected agriculture operations like map or sensor-based Variable Rate Application (VRA).

关键词： Agriculture Drones Sensors Satellites remote sensing Temperature measurement Monitoring

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Built-Up Area Extraction from Landsat 8 images Using Convolutional Neural Networks with Massive Automatically Selected Samples 1st

Built-Up Area Extraction from Landsat 8 Images Using Convolu...

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1st Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Zhang, Tao Tang, Hong Beijing Normal Univ Beijing Key Lab Environm Remote Sensing & Digital Beijing 100875 Peoples R China

ISBN: (纸本)9783030033354;9783030033347

Extraction of built-up area (e.g., roads, buildings, and other Manmade object) from remotely sensed imagery plays an important role in many urban applications. This task is normally difficult due to complex data in the form of heterogeneous appearance with large intra-class variations and lower inter-class variations. In order to extract the built-up area of 15-m resolution based on Landsat 8-OLI images, we propose the convolutional neural networks (CNN) which built in Google Drive using Colaboratory-Tensorflow. In this Framework, Google Earth Engine (GEE) provides massive remote sensing images and preprocesses the data. In this proposed CNN, for each pixel, the spectral information of the 8 bands and the spatial relationship in the 5 neighborhood are taken into account. Training this network requires lots of sample points, so we propose a method based on the ESA's 38-m global built-up area data of 2014, Open Street Map and MOD13Q1-NDVI to achieve rapid and automatic generation of large number of sample points. We choose Beijing, Lanzhou, Chongqing, Suzhou and Guangzhou of China as the experimentation sites, which represent different landforms and different urban environments. We use the proposed CNN to extract built-up area, and compare the results with other existing building data products. Our research shows: (1) The test accuracy of the five experimental sites is higher than 89%. (2) The classification results of CNN can be very good for the details of the built-up area, and greatly reduce the classification error and leakage error. Therefore, this paper provides a reference for the classification and mapping of built-up areas in large space range.

关键词： Extract built-up area CNN Landsat 8 image Generate samples Google Earth Engine Google Colaboratory

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Proceedings - 2018 15th conference on Computer and Robot Vision, CRV 2018

Proceedings - 2018 15th Conference on Computer and Robot Vis...

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15th conference on Computer and Robot Vision, CRV 2018

ISBN: (纸本)9781538664810

The proceedings contain 50 papers. The topics discussed include: real-time deep hair matting on mobile devices;occluded leaf matching with full leaf databases using explicit occlusion modeling;deep autoencoders with aggregated residual transformations for urban reconstruction from remote sensing data;real-time end-to-end action detection with two-stream networks;generalized Hadamard-product fusion operators for visual question answering;on the robustness of deep learning models to universal adversarial attack;real-time 3D face verification with a consumer depth camera;hierarchical feature map characterization in fashion interpretation;and a hierarchical deep architecture and mini-batch selection method for joint traffic sign and light detection.

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remote sensing images Classification Using Moment Features and Attribute Profiles 5

Remote Sensing Images Classification Using Moment Features a...

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IEEE International conference on Signal and image processing Applications (ICSIPA)

作者： Roochi, Niloofar Ghasemi Ghassemian, Hassan Mirzapour, Fardin Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9781509055593

remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. remote sensing is used in numerous fields, including geography, land surveying and most Earth Science disciplines. In supervised classification, all of the feature extraction methods try to increase the accuracy of classification and simultaneously time of computation. At the present work, we use the moments and Attribute Morphology Profiles (APs) to extract texture information from satellite panchromatic images. We use four conventional moments in pattern recognition such as Geometric, Chebyshev, Legendre and Zernike moments and APs to extract features from remote sensing image. An MP is constructed based on the repeated use of openings and closings by reconstruction of a structuring elements (SE) of an increasing size, applied to a scalar image. Then, we use those two set of features together. The well-known support vector machine (SVM) is used for supervised classification. We compare our proposed method with moments and APs. Different criteria such as average accuracy, overall accuracy, kappa statistic and computation time are used for assessment of classification performance.

关键词： Moment Chebyshev Geometric Legendre Zernike Attribute Morphology Profile classification support vector machine (SVM) remote sensing

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The First Automatic Method for Mapping the Pothole in Seagrass 30

The First Automatic Method for Mapping the Pothole in Seagra...

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30th IEEE/CVF conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Rahnemoonfar, Maryam Yari, Masoud Rahman, Abdullah Kline, Richard Texas A&M Univ Corpus Christi Sch Engn & Comp Sci Corpus Christi TX 78412 USA Univ Texas Rio Grande Valley Sch Earth Environm & Marine Sci Brownsville TX USA

ISBN: (纸本)9781538607336

There is a vital need to map seagrass ecosystems in order to determine worldwide abundance and distribution. Currently there is no established method for mapping the pothole or scars in seagrass. Detection of seagrass with optical remote sensing is challenged by the fact that light is attenuated as it passes through the water column and reflects back from the benthos. Optical remote sensing of seagrass is only possible if the water is shallow and relatively clear. In reality, coastal waters are commonly turbid, and seagrasses can grow under 10 meters of water or even deeper. One of the most precise sensors to map the seagrass disturbance is side scan sonar. Underwater acoustics mapping produces a high definition, two-dimensional sonar image of seagrass ecosystems. This paper proposes a methodology which detects seagrass potholes in sonar images. Side scan sonar images usually contain speckle noise and uneven illumination across the image. Moreover, disturbance presents complex patterns where most segmentation techniques will fail. In this paper, the quality of image is improved in the first stage using adaptive thresholding and wavelet denoising techniques. In the next step, a novel level set technique is applied to identify the pothole patterns. Our method is robust to noise and uneven illumination. Moreover it can detect the complex pothole patterns. We tested our proposed approach on a collection of underwater sonar images taken from Laguna Madre in Texas. Experimental results in comparison with the ground-truth show the efficiency of the proposed method.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Quality enhancement of satellite images and its application for identification of surroundings of waste disposal sites 5

Quality enhancement of satellite images and its application ...

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5th International conference on remote sensing and Geoinformation of the Environment (RSCy)

作者： Richter, Andrey Kazaryan, Maretta Shakhramanyan, Mihail Nedkov, Roumen Borisova, Denitsa Stankova, Nataliya Ivanova, Iva Zaharinova, Mariana Researching Inst AEROCOSMOS Gorohovskiy Line 4 Moscow 105064 Russia Financial Univ Govt Russian Federat Vladikavkaz Branch Molodeghnaya Str 7 Vladikavkaz 362001 Russia Bulgarian Acad Sci Space Res & Technol Inst Acad G Bonchev StBl 1 BU-1113 Sofia Bulgaria

ISBN: (纸本)9781510613539;9781510613522

The paper proposes a method for fuzzy interactive enhancement of objects identification in the image which allows identifying hidden or no defined details and objects in the images. The application of the method and its difference from other image enhancement techniques are shown. The paper presents the algorithm and describes the basic processing procedures (sampling, scaling, convolution, contrast). The main processing parameters (increasing and reduction of dimensions, convolutions, brightness, and thresholds contrast) are demonstrated. The results from the applied algorithm are explained on an example related to landfill Kutchino in the Moscow region, on the satellite images with low and high spatial resolution.

关键词： satellite images waste disposal facilities landfills pattern recognition image quality 3D-modeling

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Multi-temporal and Multi-view Based remote sensing image Registration for Ground Surface Objects Change Monitoring 13

Multi-temporal and Multi-view Based Remote Sensing Image Reg...

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2017 13th IEEE International conference on Electronic Measurement & Instruments (ICEMI)

作者： Gao Xueyan Liu Xinang Yang Yang Hong Liang School of Information Science and Technology Yunnan Normal University The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China Yunnan Normal University Laboratory of Pattern Recognition and Artificial Intelligence Yunnan Normal University The Asset Management Department The First Affiliated Hospital of Kunming Medical University School of Tourism and Geographical Sciences Yunnan Normal University

ISBN: (纸本)9781509050369;9781509050352

remote sensing image registration is a key technology in the field of remote sensing image processing. However, the change of time and the change of view angle will cause the non-rigid distortion and low degree of overlap between the images, and further increase the difficulty of image registration. In order to solve the above problems, a registration algorithm of remote sensing image based on image geometric structural and gray information is presented. By comparing the multi-temporal and multi-angle remote sensing images with four similar algorithms(SIFT, CPD, RSOC, GLMDTPS), our algorithm is superior to other algorithms in the comparison experiment, and shows the optimal registration performance.

关键词： remote sensing image multi-temporal multi-view geomtric characteristics structure non-rigid distortion

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Survey of Drones for Agriculture Automation from Planting to Harvest

Survey of Drones for Agriculture Automation from Planting to...

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IEEE International conference on Intelligent Engineering Systems

作者： Marck Kulbacki Jakub Segen Wojciech Kniec Ryszard Klempous Konrad Kluwak Jan Nikodem Julita Kulbacka Andrea Serester DIVE IN AI Wroclaw Poland Faculty of Electronics Wroclaw University of Science and Technology Wroclaw Poland Department of Molecular and Cellular Biology Wroclaw Medical University Wroclaw Poland Antal Bejczy Center for Intelligent Robotics Óbuda University Budapest Hungary

关键词： Agriculture Drones Sensors Satellites remote sensing Temperature measurement Monitoring

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Learning Deep Features on Multiple Scales for Coffee Crop recognition 30

Learning Deep Features on Multiple Scales for Coffee Crop Re...

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30th conference on Graphics, patterns and images (SIBGRAPI)

作者： Baeta, Rafael Nogueira, Keiller Menotti, David dos Santos, Jefersson A. Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil Univ Fed Parana Dept Informat Curitiba Parana Brazil

ISBN: (纸本)9781538622193

Geographic mapping of coffee crops by using remote sensing images and supervised classification has been a challenging research subject. Besides the intrinsic problems caused by the nature of multi-spectral information, coffee crops are non-seasonal and usually planted in mountains, which requires encoding and learning a huge diversity of patterns during the classifier training. In this paper, we propose a new approach for automatic mapping coffee crops by combining two recent trends on pattern recognition for remote sensing applications: deep learning and fusion/selection of features from multiple scales. The proposed approach is a pixel-wise strategy that consists in the training and combination of convolutional neural networks designed to receive as input different context windows around labeled pixels. Final maps are created by combining the output of those networks for a non-labeled set of pixels. Experimental results show that multiple scales produces better coffee crop maps than using single scales. Experiments also show the proposed approach is effective in comparison with baselines.

关键词： Deep Learning remote sensing Coffee Crops High-resolution images Agriculture

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Hyperspectral Data Compression Framework for Earth remote sensing Objectives 13th

Hyperspectral Data Compression Framework for Earth Remote Se...

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13th International conference on pattern recognition and Information processing (PRIP)

作者： Doudkin, Alexander Podenok, Leonid Pertsau, Dmitry United Inst Informat Problems Minsk BELARUS Belarusian State Univ Informat & Radioelect Minsk BELARUS

ISBN: (纸本)9783319542201;9783319542195

The hyperspectral data compression framework to well investigate various compression models is presented. Results received with arithmetic encoder, context-adaptive QM-encoder, adaptive Huffman encoder are adduced. As a test data the Maine frame set from the AVIRIS freely available data was used. The received results testify the efficiency of the proposed framework in comparison with some alternative lossless compression algorithms.

关键词： Hyperspectral data Fourier Transform Imaging Spectrometer Arithmetic coding Context-adaptive QM-encoder Adaptive Huffman encoder AVIRIS

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