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检索条件"任意字段=5th IAPR Workshop on Pattern Recognition in Remote Sensing"

共 156 条记录，以下是31-40 订阅

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An Elegant End-to-End Fully Convolutional Network (E3FCN) for Green Tide Detection using MODIS Data 10

An Elegant End-to-End Fully Convolutional Network (E3FCN) fo...

引用

10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Yin, Haoyu Liu, Yingjian Chen, Qiang Ocean Univ China Dept Comp Sci & Technol Qingdao Shandong Peoples R China

ISBN: (纸本)9781538684795

Using remote sensing (RS) data to monitor the onset, proliferation and decline of green tide (GT) has great significance for disaster warning, trend prediction and decision-making support. However, remote sensing images vary under different observing conditions, which bring big challenges to detection missions. this paper proposes an accurate green tide detection method based on an Elegant End-to-End Fully Convolutional Network (E3FCN) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In preprocessing, RS images are firstly separated into subimages by a sliding window. To detect GT pixels more efficiently, the original Fully Convolutional Neural Network (FCN) architecture is modified into E3FCN, which can be trained end-to-end. the E3FCN model can be divided into two parts, contracting path and expanding path. the contracting path aims to extract high-level features and the expanding path aims to provide a pixel-level prediction by using a skip technique. the prediction result of whole image is generated by merging the prediction results of subimages, which can also improve the final performance. Experiment results show that the average precision of E3FCN on the whole data sets is 98.06%, compared to 73.27% of Support Vector Regression (SVR), 71.75% of Normalized Difference Vegetation Index (NDVI), and 64.41% of Enhanced Vegetation Index (EVI).

关键词： green tide deep learning remote sensing Elegant End-to-End Fully Convolutional Network (E3FCN) Moderate Resolution Imaging Spectroradiometer (MODIS)

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Preliminary Investigation on Single remote sensing Image Inpainting through a Modified GAN 10

Preliminary Investigation on Single Remote Sensing Image Inp...

引用

10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Lou, Shenlong Fan, Qiancong Chen, Feng Wang, Cheng Li, Jonathan Xiamen Univ Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China

ISBN: (纸本)9781538684795

Because of impacts resulted from sensor malfunction and clouds, there is usually a great deal of missing regions (pixels) in remotely sensed imagery. To make full use of the remotely sensed imagery affected, different algorithms for remote sensing images inpainting have been proposed. In this paper, an unsupervised convolutional neural network (CNN) context generate model was modified to recover the affected (or un-recorded) pixels in a single image without auxiliary information. Unlike existing nonparametric algorithms in which pixels located in surrounding region are used to estimate the unrecorded pixel, the proposed method directly generates content based on a neural network. To ensure recovered results with high quality, a modified reconstruction loss was used in training the model, which included structural similarity index (SSIM) loss and L1 loss. Comparison of the proposed model with bilinear interpolation was indicated through relative error. the performances of two methods in scenes with different complexity were discussed further. Results show that the proposed model performed better in simple scenes (i.e., with relative homogeneity), compared to the traditional method. Meanwhile, the corrupted images of channel blue were recovered more accurately, compared to the corrupted images of other channels (i.e., channel green and channel red). the relationship between scene complexities and channels shows that same scene has different complexities in different channels. the scene complexity presents significant correlation with recovered results, high complexity images are always accompanied by poor recovered results. It suggests that the recovering accuracy depends on scene complexity.

关键词： Image inpainting unsupervised CNN bilinear interpolation SSIM

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Analysis of the preferences of public transport passengers in the task of building a personalized recommender system 5

Analysis of the preferences of public transport passengers i...

引用

5th Information Technology and Nanotechnology 2019: Image Processing and Earth remote sensing, ITNT 2019

作者： Borodinov, A.A. Myasnikov, V.V. Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia Image Processing Systems Institute of RAS - Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardejskaya street 151 Samara443001 Russia

the paper presents the theoretical and algorithmic aspects for making a personalized recommender system (mobile service) designed for public route transport users. the main focus is on identifying and formalizing the concept of "user preferences", which is the basis of modern personalized recommender systems. Informal (verbal) and formal (mathematical) formulations of the corresponding problems of determining "user preferences" in a specific spatial-temporal context are presented: the preferred stops definition and the preferred "transport correspondence" definition. the first task can be represented as a well-known classification problem. thus, it can be formulated and solved using well-known pattern recognition and machine learning methods. the second is reduced to the construction of dynamic graphs series. the experiments were conducted on data from the mobile application "Pribyvalka-63". the application is the *** service part, currently used to inform Samara residents about the public transport movement. © 2019 CEUR-WS. All rights reserved.

关键词： pattern recognition

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Instance Segmentation of Trees in Urban Areas from MLS Point Clouds Using Supervoxel Contexts and Graph-based Optimization 10

Instance Segmentation of Trees in Urban Areas from MLS Point...

引用

10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Xu, Yusheng Sun, Zhenghao Hoegner, Ludwig Stilla, Uwe Yao, Wei Tech Univ Munich Photogrammetry & Remote Sensing Munich Germany Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hung Hom Hong Kong Peoples R China

ISBN: (纸本)9781538684795

In this paper, an instance segmentation method for tree extraction from MLS data sets in urban scenes is developed. the proposed method utilizes a supervoxel structure to organize the point clouds, and then extracts the detrended geometric features from the local context of supervoxels. Combined with the detrended features of the local context, the Random Forest (RF) classifier will be adopted to obtain the initial semantic labeling results of trees from point clouds. Afterwards, a local context-based regularization is iteratively performed to achieve global optimum on a global graphical model, in order to spatially smoothing the semantic labeling results. Finally, a graph-based segmentation is conducted to separate individual trees according to the semantic labeling results. the use of supervoxel structure can preserve the geometric boundaries of objects in the scene, and compared with point-based solutions, the supervoxel-based method can largely decrease the number of basic elements during the processing. Besides, the introduction of supervoxel contexts can extract the local information of an object making the feature extraction more robust and representative. Detrended geometric features can get over the redundant and in-salient information in the local context, so that discriminative features are obtained. Benefiting from the regularization process, the spatial smoothing is obtained based on initial labeling results from classic classifications such as RF classification. As a result, misclassification errors are removed to a large degree and semantic labeling results are thus smoothed. Based on the constructed global graphical model during the spatially smoothing process, a graph-based segmentation is applied to partition the graphical model for the clustering the instances of trees. the experiments on two test datasets have shown promising results, with an accuracy of the semantic labeling of trees reaching around 0.9. the segmentation of trees using graph-bas

关键词： Instance segmentation MLS trees urban areas supervoxels local context graph-based segmentation

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A method of Interactively Extracting Region Objects from high-resolution remote sensing image based on full connection CRF

A method of Interactively Extracting Region Objects from hig...

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10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Zhang Chunsen Yu Zhen Hu Yan Xian Univ Sci & Technol Coll Geomat Xian Shaanxi Peoples R China

ISBN: (纸本)9781538684795

Aiming at the region objects of high resolution remote sensing images, this paper proposes an interactive region objects extraction method for high-resolution remote sensing images based on fully connected conditional random fields. this method estimates the foreground model by artificial interaction markers. On the basis of using the SLIC algorithm to over segment the input images, combining the color and texture features, the region-based maximum similarity fusion (MSRM) is used to expand the foreground region and establish the global information of the full-connection conditional random field description image. then, based on the mean-field estimation, the model inference is realized by the high-dimensional Gauss filtering method, and then the contour of the area features is obtained. the experimental results show that the method is effective by extracting the area features such as waters, woodlands, terraces and bare lands on high resolution remote sensing images.

关键词： high-resolution remote sensing image full-connected conditional random field over segmentation feature extraction mean field estimation

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Extraction of Altered Mineral from remote sensing Data in Gold Exploration based on the Nonlinear Analysis Technology

Extraction of Altered Mineral from Remote Sensing Data in Go...

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10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Han Haihui Wang Yilin Zhang Zhuan Ren Guangli Yang Min Geol Survey China Xian Ctr Geol Survey Xian 710054 Shaanxi Peoples R China

ISBN: (纸本)9781538684795

Researchers have found that the mixed pixels exist in complex geological conditions often lead to distortion of altered minerals' spectral curves, and the accuracy of altered mineral extract from remote sensing data was reduced. Fortunately, the nonlinear analysis is a feasible solution. In this paper, by analyzing the nonlinear characteristics of the geological anomalies, the Fractal Dimension Change Point Method (FDCPM) will be used to extract the altered minerals' threshold from multispectral image. the realization theory and access mechanism of the model are elaborated by an experiment with ASTER data in Xinjinchang and Laojinchang gold deposits. the results show that the findings produced by FDCPM are agreed with well with a mounting body of evidence from different perspectives. the extracting accuracy over 86% show that FDCPM is an effective extracting method for remote sensing alteration anomalies, and it could be used as an useful tool for mineral exploration in similar areas in Beishan mineralization belt in northwest China.

关键词： Nonlinear Analysis Aster Self-similarity Mutability Exploration

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Deep Cross-Modal Retrieval for remote sensing Image and Audio

Deep Cross-Modal Retrieval for Remote Sensing Image and Audi...

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10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Guo Mao Yuan Yuan Lu Xiaoqiang Chinese Acad Sci Xian Inst Opt & Precis Mech Ctr Opt IMagery Anal & Learning OPTIMAL Xian 710119 Shaanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781538684795

remote sensing image retrieval has many important applications in civilian and military fields, such as disaster monitoring and target detecting. However, the existing research on image retrieval, mainly including to two directions, text based and content based, cannot meet the rapid and convenient needs of some special applications and emergency scenes. Based on text, the retrieval is limited by keyboard inputting because of its lower efficiency for some urgent situations and based on content, it needs an example image as reference, which usually does not exist. Yet speech, as a direct, natural and efficient human-machine interactive way, can make up these shortcomings. Hence, a novel cross-modal retrieval method for remote sensing image and spoken audio is proposed in this paper. We first build a large-scale remote sensing image dataset with plenty of manual annotated spoken audio captions for the cross-modal retrieval task. then a Deep Visual-Audio Network is designed to directly learn the correspondence of image and audio. And this model integrates feature extracting and multi-modal learning into the same network. Experiments on the proposed dataset verify the effectiveness of our approach and prove that it is feasible for speech-to-image retrieval.

关键词： cross-modal retrieval remote sensing image spoken audio convolutional neural network

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A method of interactively extracting region objects from high-resolution remote sensing image based on full connection CRF 10

A method of interactively extracting region objects from hig...

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10th iapr workshop on pattern recognition in remote sensing, PRRS 2018

作者： Chunsen, Zhang Zhen, Yu Yan, Hu College of Geomatics Xi'An University of Science and Technology China

ISBN: (纸本)9781538684795

关键词： Image segmentation

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SAR image matching area selection based on actual flight real-time image

SAR image matching area selection based on actual flight rea...

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10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Wang Jianmei Wang Zhong Zhang Shaoming Feng Tiantian Dong Jihui Tongji Univ Coll Surveying & Geoinformat Comp Vis & Remote Sensing Grp Shanghai Peoples R China

ISBN: (纸本)9781538684795

Matching suitability analysis is a key issue of INS/SAR integrated navigation mode. the existing suitability area selection methods use the simulated real-time image to calculate the matching probability of the scene area and further label it "suitability" or "unsuitability". If the imaging mode of the simulated image is the same as that of the real image, the suitability area selection model based on the simulated real-time image works well. Otherwise, the model is impractical. In order to address this issue, a novel method is proposed in this paper. the sample dataset is built on the actual flight real-time images, and a hybrid feature selection method based on D-Score and SVM is used to select the suitability features and build the suitability area selection model simultaneously. Experimental results show that the consistency between the prediction results of the model and the ones experts label reaches 81.92%.

关键词： INS/SAR integrated navigation suitability area selection suitability features real-time image feature selection

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Decomposition of Landsat 8 OLI Images by Simplified Spectral patterns for Land Cover Mapping

Decomposition of Landsat 8 OLI Images by Simplified Spectral...

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10th iapr workshop on pattern recognition in remote sensing (PRRS)

作者： Nguyen Dinh Duong Vietnam Acad Sci & Technol Inst Geog 18 Hoang Quoc Viet Rd Hanoi Vietnam

ISBN: (纸本)9781538684795

Decomposition of multispectral image data has recently become the focus of remote sensing scientists because it improves efficiency of land cover mapping by data reduction. the three main traditional techniques used for decomposition are principal component analysis, linear spectral mixing, and pattern decomposition. this paper presents a new method of pattern decomposition using simplified spectral patterns. Spectral characteristics of land cover are analyzed using the shape of their spectral reflectance curves, leading to decomposition of an image into component images with pixels that are characterized by spectral reflectance curves of the same shape. the author proposes a method for converting a spectral reflectance curve from an analog to a simplified digital form consisted of 15 digits of zeros, ones, and twos. this form of the spectral reflectance curve is called simplified spectral pattern (SSP). One hundred Landsat 8 OLI scenes of 14 global biomes were collected for the research. the study found that 34 dominant simplified spectral patterns represent land cover types in selected scenes, covering 96.53% of the total 4,150,671,513 pixels. SSP correlates with single land cover types such as water, ice, and snow or with multiple land cover types such as vegetation, minerals, rock, and soil.

关键词： decomposition spectral reflectance curve simplified spectral pattern land cover classification

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