International Journal of Image and Data Fusion最新文献_第10页

Machine learning on high performance computing for urban greenspace change detection: satellite image data fusion approach 基于高性能计算的机器学习城市绿地变化检测:卫星图像数据融合方法

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2020-04-10 DOI: 10.1080/19479832.2020.1749142

Nilkamal More, V. Nikam, Biplab Banerjee

{"title":"Machine learning on high performance computing for urban greenspace change detection: satellite image data fusion approach","authors":"Nilkamal More, V. Nikam, Biplab Banerjee","doi":"10.1080/19479832.2020.1749142","DOIUrl":"https://doi.org/10.1080/19479832.2020.1749142","url":null,"abstract":"ABSTRACT Green spaces serve important environmental and quality-of-life functions in urban environments. Fast-changing urban regions require continuous and fast green space change detection. This study focuses on assessment of green space change detection using GPU- for time efficient green space identification and monitoring. Using spatio-temporal data from satellite images and a support vector machine (SVM) as a classification algorithm, this research proposes a platform for green space analysis and change detection. The main contributions of this research include the fusion of the thermal band in addition to Near infra-red, red, green band with the fusion of high spectral information of the moderate resolution imaging spectroradiometer (MODIS) dataset and high spatial information of the LANDSAT 7 dataset. The novel method is employed to calculate the total green space area in the Mumbai metropolitan area and monitor the changes from 2005–2019. This research paper discusses the findings of our strategy and reveals that over the course of 15 years the overall green space was reduced to 50%.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1749142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44491658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

A multi-sensor-based evaluation of the morphometric characteristics of Opa river basin in Southwest Nigeria 基于多传感器的尼日利亚西南部Opa河流域形态特征评估

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2020-04-02 DOI: 10.1080/19479832.2019.1683622

A. O. Adewole, Felix Ike, A. Eludoyin

{"title":"A multi-sensor-based evaluation of the morphometric characteristics of Opa river basin in Southwest Nigeria","authors":"A. O. Adewole, Felix Ike, A. Eludoyin","doi":"10.1080/19479832.2019.1683622","DOIUrl":"https://doi.org/10.1080/19479832.2019.1683622","url":null,"abstract":"ABSTRACT Studies have shown that many river basins in the sub-Saharan Africa are largely unmonitored, partly because they are poorly or totally ungauged. In this study, remote sensing products (Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer; ASTER and Shuttle Radar Topography Mission; SRTM) that are freely available in the region were harnessed for the monitoring of Opa river basin in southwestern Nigeria. The remote sensing products were complementarily used with topographical sheets (1:50,000), ground based observation and global positioning systems to determine selected morphometric characteristics as well as changes in landuse/landcover and its impact on peak runoff in the Opa river basin. Results showed that the basin is a 5th order basin whose land area has been subjected to different natural and anthropogenic influences within the study period. Urbanisation is a major factor that threatens the basin with degradation and observed changes, and the threats are expected to become worse if restoration is not considered from some tributaries. The study concluded that commentary use of available remote sensing products in the region will provide an important level of decision support information for management and monitoring of river basins.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2019.1683622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47608661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Video-based salient object detection using hybrid optimisation strategy and contourlet mapping 基于混合优化策略和contourlet映射的视频显著目标检测

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2020-04-02 DOI: 10.1080/19479832.2019.1683625

S. A., H. N. Suresh

{"title":"Video-based salient object detection using hybrid optimisation strategy and contourlet mapping","authors":"S. A., H. N. Suresh","doi":"10.1080/19479832.2019.1683625","DOIUrl":"https://doi.org/10.1080/19479832.2019.1683625","url":null,"abstract":"ABSTRACT The advancements in salient object detection have attracted many researchers and are significant in several computer vision applications. However, efficient salient object detection using still images is a major challenge. This paper proposes salient object detection technique using the proposed Spider-Gray Wolf Optimiser (S-GWO) algorithm that is designed by combining Gray Wolf Optimiser (GWO) and Spider Monkey Optimisation (SMO). The technique undergoes three steps, which involves keyframe extraction, saliency mapping, contourlet mapping, and then, fusion of obtained outputs using optimal coefficients. Initially, the extracted frames are subjected to saliency mapping and contourlet mapping simultaneously in order to determine the quality of each pixel. Then, the outputs obtained from the saliency mapping and contourlet mapping is fused using the arbitrary coefficients for obtaining the final result that is employed for detecting the salient objects. Here, the proposed S-GWO is employed for selecting the optimal coefficients for detecting the salient objects. The experimental evaluation of the proposed S-GWO based on the performance metrics reveals that the proposed S-GWO attained a maximal accuracy, sensitivity and specificity with 0.914, 0.861 and 0.929, respectively.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2019.1683625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44951669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Improved auto-extrinsic calibration between stereo vision camera and laser range finder 改进了立体视觉相机与激光测距仪之间的自外部标定

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2020-02-28 DOI: 10.1080/19479832.2020.1727574

Archana Khurana, K. S. Nagla

{"title":"Improved auto-extrinsic calibration between stereo vision camera and laser range finder","authors":"Archana Khurana, K. S. Nagla","doi":"10.1080/19479832.2020.1727574","DOIUrl":"https://doi.org/10.1080/19479832.2020.1727574","url":null,"abstract":"ABSTRACT This study identifies a way to accurately estimate extrinsic calibration parameters between stereo vision camera and 2D laser range finder (LRF) based on 3D reconstruction of monochromatic calibration board and geometric co-planarity constraints between the views from these two sensors. It supports automatic extraction of plane-line correspondences between camera and LRF using monochromatic board, which is further improved by selecting optimal threshold values for laser scan dissection to extract line features from LRF data. Calibration parameters are then obtained by solving co-planarity constraints between the estimated plane and line. Furthermore, the obtained parameters are refined by minimising reprojection error and error from the co-planarity constraints. Moreover, calibration accuracy is achieved because of extraction of reliable plane-line correspondence using monochromatic board which reduces the impact of range-reflectivity-bias observed in LRF data on checkerboard . As the proposed method supports to automatically extract feature correspondences, it provides a major reduction in time required from an operator in comparison to manual methods. The performance is validated by extensive experimentation and simulation, and estimated parameters from the proposed method demonstrate better accuracy than conventional methods.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1727574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42560550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An optimised multi-scale fusion method for airport detection in large-scale optical remote sensing images 大尺度光学遥感影像机场检测的多尺度融合优化方法

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2020-02-20 DOI: 10.1080/19479832.2020.1727573

Shoulin Yin, Hang Li, Lin Teng, Man Jiang, Shahid Karim

{"title":"An optimised multi-scale fusion method for airport detection in large-scale optical remote sensing images","authors":"Shoulin Yin, Hang Li, Lin Teng, Man Jiang, Shahid Karim","doi":"10.1080/19479832.2020.1727573","DOIUrl":"https://doi.org/10.1080/19479832.2020.1727573","url":null,"abstract":"ABSTRACT Airport detection in remote sensing images is an important process which plays a significant role in military and civil areas. Mostly, conventional algorithms have been used for airport detection from a small-scale remote sensing image and revealed the less efficient ability of searching the object from a large-scale high-resolution remote sensing image. The computational complexity of these algorithms is high and these are not useful for rapid localisation with high detection accuracy in high-resolution remote sensing images. Aiming to solve the above problems, we propose an optimised multi-scale fusion method for airport detection in large-scale optical remote sensing images. Firstly, we execute discrete wavelet multi-scale decomposition for remote sensing image and extract the multiple features of the object in each sub-band. Secondly, the fusion rule based on the optimised region selection is used to fuse the features on each scale. Meanwhile, singular-value decomposition (SVD) is utilised for fusing low-frequency and principal component analysis (PCA) is utilised to fuse the high-frequency, respectively. Thirdly, the final-fused image is acquired by weighted fusion. Finally, the selective search method is employed to detect the airport in the fused image. Experimental results show that the detection accuracy is better than the other state-of-the-art methods.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1727573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44338123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

Creating a virtual reality environment with a fusion of sUAS and TLS point-clouds 创建融合sUAS和TLS点云的虚拟现实环境

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2020-01-29 DOI: 10.1080/19479832.2020.1716861

D. Bolkas, Jeffrey Chiampi, J. Chapman, Vincent F. Pavill

{"title":"Creating a virtual reality environment with a fusion of sUAS and TLS point-clouds","authors":"D. Bolkas, Jeffrey Chiampi, J. Chapman, Vincent F. Pavill","doi":"10.1080/19479832.2020.1716861","DOIUrl":"https://doi.org/10.1080/19479832.2020.1716861","url":null,"abstract":"ABSTRACT In recent years, immersive virtual reality has been used in disciplines such as engineering, sciences, and education. Point-cloud technologies such as laser scanning and unmanned aerial systems have become important for creating virtual environments. This paper discusses creating virtual environments from 3D point-cloud data suitable for immersive and interactive virtual reality. Both laser scanning and sUAS point-clouds are utilised. These point-clouds are merged using a custom-made algorithm that identifies data gaps in the master dataset (laser scanner) and fills them with data from a slave dataset (sUAS) resulting in a more complete dataset that is used for terrain modelling and 3D modelling of objects. The terrain and 3D objects are then textured with custom-made and free textures to provide a sense of realism in the objects. The created virtual environment is a digital copy of a part of the Penn State Wilkes-Barre campus. This virtual environment will be used in immersive and interactive surveying laboratories to assess the role of immersive virtual reality in surveying engineering education.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1716861","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43747254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Accurate playground localisation based on multi-feature extraction and cascade classifier in optical remote sensing images 基于多特征提取和级联分类器的光学遥感影像运动场精确定位

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2020-01-25 DOI: 10.1080/19479832.2020.1716862

Xiaowei Wang, Shoulin Yin, Desheng Liu, Hang Li, Shahid Karim

{"title":"Accurate playground localisation based on multi-feature extraction and cascade classifier in optical remote sensing images","authors":"Xiaowei Wang, Shoulin Yin, Desheng Liu, Hang Li, Shahid Karim","doi":"10.1080/19479832.2020.1716862","DOIUrl":"https://doi.org/10.1080/19479832.2020.1716862","url":null,"abstract":"ABSTRACT To address the low accuracy problem of playground detection under complex background, the accurate playground localization based on multi-feature extraction and cascade classifier is proposed in this paper. It is difficult to utilize this information to separate objects from the complex background. Therefore, we adopt multi-feature extraction method to make the playgrounds more easily to be detected. The proposed localization method is partitioned into two modules: feature extraction and classification. First, multi feature extraction method combining histogram of oriented gradients (HOG) and Haar is utilized to extract features from raw images. HOG can authentically capture the shape information, which is extracted to characterize the local region. Haar can improve the image eigenvalue calculation effectively. Afterwards, cascade classifier based on AdaBoost algorithm is adopted to classify the extracted features. Finally we conduct the experiments with our proposed methodology on a publicly accessible remote sensing images from Google Earth. The results demonstrate that the proposed framework has a better effect with achieving high levels of recall, precision and F-score compared to the state-of-the-art alternatives, without sacrificing computational soundness. What is more, the results indicate that the proposed playground 1ocalisation method has strong robustness under different complex backgrounds with high detection rate.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1716862","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47839197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

An overview of deep learning methods for image registration with focus on feature-based approaches 图像配准的深度学习方法概述，重点介绍基于特征的方法

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2020-01-06 DOI: 10.1080/19479832.2019.1707720

Kavitha Kuppala, Sandhya Banda, Thirumala Rao Barige

引用次数: 26

Classification of SAR and PolSAR images using deep learning: a review 使用深度学习对SAR和PolSAR图像进行分类：综述

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2020-01-02 DOI: 10.1080/19479832.2019.1655489

Hemani Parikh, Samir B. Patel, Vibha Patel

{"title":"Classification of SAR and PolSAR images using deep learning: a review","authors":"Hemani Parikh, Samir B. Patel, Vibha Patel","doi":"10.1080/19479832.2019.1655489","DOIUrl":"https://doi.org/10.1080/19479832.2019.1655489","url":null,"abstract":"ABSTRACT Advancement in remote sensing technology and microwave sensors explores the applications of remote sensing in different fields. Microwave remote sensing encompasses its benefits of providing cloud-free, all-weather images and images of day and night. Synthetic Aperture Radar (SAR) images own this capability which promoted the use of SAR and PolSAR images in land use/land cover classification and various other applications for different purposes. A review of different polarimetric decomposition techniques for classification of different regions is introduced in the paper. The general objective of the paper is to help researchers in identifying a deep learning technique appropriate for SAR or PolSAR image classification. The architecture of deep networks which ingest new ideas in the given area of research are also analysed in this paper. Benchmark datasets used in microwave remote sensing have been discussed and classification results of those data are analysed. Discussion on experimental results on one of the benchmark datasets is also provided in the paper. The paper discusses challenges, scope and opportunities in research of SAR/PolSAR images which will be helpful to researchers diving into this area.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2019.1655489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48728383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 49

A novel region-based iterative seed method for the detection of multiple lanes 一种基于区域的多车道检测迭代种子方法

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2020-01-02 DOI: 10.1080/19479832.2019.1683623

S. Shirke, R. Udayakumar

{"title":"A novel region-based iterative seed method for the detection of multiple lanes","authors":"S. Shirke, R. Udayakumar","doi":"10.1080/19479832.2019.1683623","DOIUrl":"https://doi.org/10.1080/19479832.2019.1683623","url":null,"abstract":"ABSTRACT Most of the global automotive companies have been paid great efforts for reducing the accidents by developing an Advanced Driver Assistance System (ADAS) as well as autonomous vehicles. Lane detection is essential for both autonomous driving and ADAS because the vehicles must follow the lane. Detection of the lane is very challenging because of the varying road conditions. Lane detection has attracted the attention of the computer vision community for several decades. Essentially, lane detection is a multi-feature detection problem that has become a real challenge for computer vision and machine learning techniques. This paper presents a region-based segmentation based on iterative seed method for multi-lane detection. Here, the detection of multi-lanes is done after the segmentation, which is highly efficient and improves the computing speed. In the proposed region-based segmentation method, the segmentation of lanes from the roads is carried out by selecting the target grids, after partitioning the input image into grids. Then, based on the distance measure, the optimal segments are chosen by an iterative procedure. The performance of the proposed region-based iterative seed method is evaluated using detection accuracy, sensitivity, and specificity, where it has the maximum detection accuracy of 98.89%.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2019.1683623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49231307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3