International Journal of Image and Data Fusion最新文献_第3页

Semi-automatic road extraction from high resolution satellite images by template matching using Kullback–Leibler divergence as a similarity measure 基于Kullback-Leibler散度的高分辨率卫星图像半自动道路提取方法

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2022-09-13 DOI: 10.1080/19479832.2022.2121767

Xiangguo Lin, W. Xie, Libo Zhang, H. Sang, Jing Shen, S. Cui

{"title":"Semi-automatic road extraction from high resolution satellite images by template matching using Kullback–Leibler divergence as a similarity measure","authors":"Xiangguo Lin, W. Xie, Libo Zhang, H. Sang, Jing Shen, S. Cui","doi":"10.1080/19479832.2022.2121767","DOIUrl":"https://doi.org/10.1080/19479832.2022.2121767","url":null,"abstract":"ABSTRACT Semi-automatic extraction of roads is greatly needed to accelerate the acquisition and updating of road maps. However, road surfaces are frequently disturbed on very high spatial resolution (VHSR) remotely sensed satellite imagery, which bothers the road trackers using least-squares-based template matching. This paper presents a novel semi-automatic framework for road tracking from VHSR satellite imagery. First, a human operator inputs three seed points. Second, the computer automatically tracks the road by the template matching using Kullback–Leibler divergence as a similarity measure. At the same time, a human operator is retained in the tracking process to supervise the extracted results, to response to the program’s prompts. Once the failure or error happens, the human operator will correct the results and restart the automatic tracking. The above procedure is repeated until a whole road is tracked. Four satellite images with different complexities are used to perform experiments. The results show that our proposed road trackers is capable of automatically, accurately and fast extracting the long and high-level roads from the VHSR satellite images.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"13 1","pages":"316 - 336"},"PeriodicalIF":2.3,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42488127","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: 0

A High Dynamic Range Image Fusion Method Based on Dual Gain Image 一种基于双增益图像的高动态范围图像融合方法

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2022-08-26 DOI: 10.1080/19479832.2022.2116492

Li Yuan, Wenbo Wu, Shuli Dong, Q. He, Feiran Zhang

{"title":"A High Dynamic Range Image Fusion Method Based on Dual Gain Image","authors":"Li Yuan, Wenbo Wu, Shuli Dong, Q. He, Feiran Zhang","doi":"10.1080/19479832.2022.2116492","DOIUrl":"https://doi.org/10.1080/19479832.2022.2116492","url":null,"abstract":"ABSTRACT For a camera with automatic gain control, two images with high and low optical gain can be output at the same exposure time. Due to the small gain value, most of target details are hidden in the dark pixels for the low gain image, and the brightness saturation usually appears in high gain image for the high luminance areas. To obtain the essential information from the dual gain images, a generation method of high dynamic range image based on dual gain image was developed. The method is composed of five parts, including enhancement of image detail, establishment of Laplacian pyramid, selection of fusion operator, reconstruction of fusion pyramid and adjustment of image contrast. Results showed that combination of the gradient operator for N-1 layer and the neighbourhood filter operator for the Nth layer had better fusion effect. Moreover, based on the analysis of image information entropy and clarity, the fusion efficiency was calculated, and the fusion efficiency of Mertens’s method, Jiang’s method, Zhang’s method, Goshtasby’s method and the presented method was 30.5%, 33.5%, 39.5%, 51% and 99%, indicating that the HDR fusion method based on dual gain image is reliable.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"14 1","pages":"15 - 37"},"PeriodicalIF":2.3,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43077309","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: 0

Unsupervised self-training method based on deep learning for soil moisture estimation using synergy of sentinel-1 and sentinel-2 images 基于深度学习的无监督自训练方法用于利用sentinel-1和sentinel-2图像的协同作用估计土壤水分

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2022-07-31 DOI: 10.1080/19479832.2022.2106317

A. Ben Abbes, N. Jarray

{"title":"Unsupervised self-training method based on deep learning for soil moisture estimation using synergy of sentinel-1 and sentinel-2 images","authors":"A. Ben Abbes, N. Jarray","doi":"10.1080/19479832.2022.2106317","DOIUrl":"https://doi.org/10.1080/19479832.2022.2106317","url":null,"abstract":"ABSTRACT Here, we present a novel unsupervised self-training method (USTM) for SM estimation. First, a ML model is trained using the labeled and unlabeled data. Then, the pseudo-labeled data are generated employing the second model by adding a proxy labeled data. Eventually, SM is estimated applying the third model by pseudo-labeled data generated by the second model and unlabeled data. The final SM estimation result is obtained by training the third model. Subsequently, in-situ measurements are performed to validate our method. The final model is an unsupervised learning model. Experiments were carried out at two different sites located in southern Tunisia using Sentinel-1A and Sentinel-2A data. The input data include the backscatter coefficient in two-mode polarization ( and ), derived from Sentinel-1A, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Infrared Index (NDII) for Sentinel-2A and in-situ data. The USTM method based on (Random Forest (RF)- Convolutional neural network (CNN)-CNN) combination allowed obtaining the best performance and precision rate, compared to other combinations (Artificial Neural Network (ANN)-CNN-CNN) and (eXtreme Gradient Boosting (XGBoost)-CNN-CNN).","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"14 1","pages":"1 - 14"},"PeriodicalIF":2.3,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48358633","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: 9

Evaluation of focal loss based deep neural networks for traffic sign detection 基于焦点损失的深度神经网络交通标志检测评价

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2022-06-21 DOI: 10.1080/19479832.2022.2086304

Deepika Kamboj, Sharda Vashisth, Sumeet Saurav

{"title":"Evaluation of focal loss based deep neural networks for traffic sign detection","authors":"Deepika Kamboj, Sharda Vashisth, Sumeet Saurav","doi":"10.1080/19479832.2022.2086304","DOIUrl":"https://doi.org/10.1080/19479832.2022.2086304","url":null,"abstract":"ABSTRACT With advancements in autonomous driving, demand for stringent and computationally efficient traffic sign detection systems has increased. However, bringing such a system to a deployable level requires handling critical accuracy and processing speed issues. A focal loss-based single-stage object detector, i.e RetinaNet, is used as a trade-off between accuracy and processing speed as it handles the class imbalance problem of the single-stage detector and is thus suitable for traffic sign detection (TSD). We assessed the detector’s performance by combining various feature extractors such as ResNet-50, ResNet-101, and ResNet-152 on three publicly available TSD benchmark datasets. Performance comparison of the detector using different backbone includes evaluation parameters like mean average precision (mAP), memory allocation, running time, and floating-point operations. From the evaluation results, we found that the RetinaNet object detector using the ResNet-152 backbone obtains the best mAP, while that using ResNet-101 strikes the best trade-off between accuracy and execution time. The motivation behind benchmarking the detector on different datasets is to analyse the detector’s performance on different TSD benchmark datasets. Among the three feature extractors, the RetinaNet model trained using the ResNet-50 backbone is an excellent model in memory consumption, making it an optimal choice for low-cost embedded devices deployment.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"14 1","pages":"122 - 144"},"PeriodicalIF":2.3,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44159531","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: 0

Surface drainage features identification using LiDAR DEM smoothing in agriculture area: a study case of Kebumen Regency, Indonesia 基于激光雷达DEM平滑的农业区地表排水特征识别——以印度尼西亚Kebumen Regency为例

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2022-05-17 DOI: 10.1080/19479832.2022.2076160

H. H. Handayani, Arizal Bawasir, A. Cahyono, T. Hariyanto, H. Hidayat

{"title":"Surface drainage features identification using LiDAR DEM smoothing in agriculture area: a study case of Kebumen Regency, Indonesia","authors":"H. H. Handayani, Arizal Bawasir, A. Cahyono, T. Hariyanto, H. Hidayat","doi":"10.1080/19479832.2022.2076160","DOIUrl":"https://doi.org/10.1080/19479832.2022.2076160","url":null,"abstract":"ABSTRACT Digital Elevation Model (DEM) is the most vital data to generate drainage networks and to provide critical terrain factors and hydrologic derivatives, such as slope, aspect, and streamflow. The accuracy of generated drainage features is extensively dependent on the quality and resolution of DEM, such as LiDAR-derived DEM. Contrary, it has a high level of roughness and complexity. Thus, smoothing methods are sometimes employed to conquer the roughness. This paper presents feature-preserving DEM smoothing (FPDEM-S) and edge-preserving DEM smoothing (EPDEM-S) approaches to smooth surface complexity in kind of preserving small drainage features using the 0.5 m – resolution LiDAR DEM of the Kedungbener River area in Kebumen Regency, Indonesia. Entangling linear morphometric factors, those smoothing approaches delivered a slight difference of stream number, with the FPDEM-S stream length ratio performing 7% better tendencies. The FPDEM-S method perormed better than EPDEM-S in this study area to provide an optimal smoothed LiDAR DEM at certain parameter values. Summarising that two smoothing methods approaches performed similar characteristics of watershed as an oval structure close to the circular shape. Also, it can be revealed that the watershed did not reach maturity phase.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"14 1","pages":"182 - 203"},"PeriodicalIF":2.3,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46974091","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: 2

Reflectance spectroscopy and ASTER mapping of aeolian dunes of Shaqra and Tharmada Provinces, Saudi Arabia: Field validation and laboratory confirmation 沙特阿拉伯Shaqra和Tharmada省风成沙丘的反射光谱和ASTER制图:野外验证和实验室确认

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2022-05-01 DOI: 10.1080/19479832.2022.2069160

Yousef Salem, H. Ghrefat, R. Sankaran

{"title":"Reflectance spectroscopy and ASTER mapping of aeolian dunes of Shaqra and Tharmada Provinces, Saudi Arabia: Field validation and laboratory confirmation","authors":"Yousef Salem, H. Ghrefat, R. Sankaran","doi":"10.1080/19479832.2022.2069160","DOIUrl":"https://doi.org/10.1080/19479832.2022.2069160","url":null,"abstract":"ABSTRACT Spatial variability of grain sizes and mapping of aeolian dunes is important to study the sand erosion, transport, and dune movement and to understand the dune encroachment and land degradation. This study examines the grain size statistical parameters and mineralogical composition of 68 sand samples collected from 17 crescentic dunes and assesses the source and depositional environment of these dunes. The analyses of samples for grain sizes resulted that the sands are characteristics to fine with an average size of 2.28 Φ and classified as moderately well-sorted (0.59 Φ), mesokurtic (0.97 Φ), and fine to coarsely skewed (0.14 Φ). X-Ray Diffraction shows that the dunes are deposited mainly by quartz, calcite, and haematite. The occurrence of absorption features near 0.5, 0.9, and 2.22 μm confirm the presence of such iron and aluminosilicate minerals in the dunes. The dunes of the provinces were mapped using TIR bands of ASTER satellite data by Carbonate index (CI) and Quartz index (QI). A good agreement among the results of grain size analyses, spectral measurements, mineralogical studies, and mapping of dunes with the field observations suggests that the sand deposits in the study area have a diversity of sources in the aeolian environment.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"14 1","pages":"162 - 181"},"PeriodicalIF":2.3,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44619918","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

Performance analysis of parameter estimator on non-linear iterative methods for ultra-wideband positioning 非线性迭代法超宽带定位参数估计器性能分析

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2022-04-12 DOI: 10.1080/19479832.2022.2064554

Chuanyang Wang, Bing He, Liangliang Shi, Weiduo Huang, Liuxu Shan

{"title":"Performance analysis of parameter estimator on non-linear iterative methods for ultra-wideband positioning","authors":"Chuanyang Wang, Bing He, Liangliang Shi, Weiduo Huang, Liuxu Shan","doi":"10.1080/19479832.2022.2064554","DOIUrl":"https://doi.org/10.1080/19479832.2022.2064554","url":null,"abstract":"ABSTRACT Ultra-wideband is a promising technology in indoor positioning due to its accurate time resolution and good penetration. Since the positioning model is non-linear, iterative methods are often considered for solving the localisation problem. However, the positioning system is prone to become ill-posed. The iterative methods cannot easily converge to a global optimal solution. In this paper, the convergence property of four non-linear iterative methods is analytically reviewed under ill-conditioned configuration. For the iteration, three types of initial values are selected. Experimental results are given to demonstrate that although the barycentre method can converge correctly, it is inefficient with too many iterations. In addition, with a good initial value, the Gauss–Newton method can converge effectively, and it sometimes converges to a false local optimisation solution when selecting a bad initial value. Moreover, both the regularised Gauss–Newton method and closed-form Newton method can converge to the global optimum effectively with fewer iterations. This study shows that the closed-form Newton method has higher efficiency of convergence than the other methods. Meanwhile, to make complete use of measurements available to improve the accuracy, the result of non-iterative method is generally used as the initial value of the iterative method.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"14 1","pages":"145 - 161"},"PeriodicalIF":2.3,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43498204","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

Estimating Leaf Area Index and biomass of sugarcane based on Gaussian process regression using Landsat 8 and Sentinel 1A observations 基于高斯过程回归的Landsat 8和Sentinel 1A观测资料估算甘蔗叶面积指数和生物量

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2022-03-25 DOI: 10.1080/19479832.2022.2055157

Gebeyehu Abebe, T. Tadesse, B. Gessesse

{"title":"Estimating Leaf Area Index and biomass of sugarcane based on Gaussian process regression using Landsat 8 and Sentinel 1A observations","authors":"Gebeyehu Abebe, T. Tadesse, B. Gessesse","doi":"10.1080/19479832.2022.2055157","DOIUrl":"https://doi.org/10.1080/19479832.2022.2055157","url":null,"abstract":"ABSTRACT Accurate estimation of crop parameters, such as Leaf Area Index (LAI) and biomass over large areas using remote sensing techniques, is crucial for monitoring crop growth and yield prediction. In this study, a Gaussian Process Regression (GPR) method was developed to estimate LAI and biomass values of sugarcane during growth season using optical and synthetic-Aperture Radar (SAR) data fusion. Predicting LAI on an independent test data set using the GPR and the combined optical and SAR indices provided better prediction accuracies of LAI; with the GPR based on radial basis function (Root Mean Square Error [RMSE] = 0.34, Mean Absolute Error [MAE] = 0.28 and Mean Absolute Percentage Error [MAPE] = 10.5%) and polynomial function (RMSE = 0.42, MAE = 0.31 and MAPE = 12.58%), respectively. The test results of sugarcane biomass also showed that the GPR (poly) produced the highest statistical results (RMSE = 2.45 kg/m2, MAE = 1.72 kg/m2, MAPE = 8.1%) using the combined indices. The results suggest that the crop biophysical retrieval based on optical and SAR data fusion and GPR proposed in this study could improve LAI and biomass estimation that could help for effective crop growth monitoring and mapping applications.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"14 1","pages":"58 - 88"},"PeriodicalIF":2.3,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42895966","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: 6

A segment-based filtering method for mobile laser scanning point cloud 一种基于分段的移动激光扫描点云滤波方法

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2022-03-06 DOI: 10.1080/19479832.2022.2047801

Xiangguo Lin, W. Xie

引用次数: 3

Augmentation Method for anti-vibration hammer on power transimission line based on CycleGAN 基于CycleGAN的输电线路减振锤增强方法

IF 2.3

International Journal of Image and Data Fusion Pub Date : 2022-02-22 DOI: 10.1080/19479832.2022.2033855

Ya-Guang Tian, Yuan-Wei Chen, Wan Diming, Yuan Shaoguang, Mao Wandeng, Wang Chao, Chun-xiao Xu, Yifan Long

引用次数: 2