Spatio-temporal-spectral observation model for urban remote sensing

Shao, Zhenfeng; Wu, Wenfu; Li, Deren

doi:10.1080/10095020.2020.1864232

Cited by 50 publications

(14 citation statements)

References 35 publications

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“…Additionally, novel techniques using deep learning algorithms can utilize the spectral, spatial, and temporal characteristics of multi-source imagery, including optical, SAR, LiDAR, and even street view data. Several studies have shown its successful application to urban areas and crop-type delineation, potentially applicable to mapping the various rice ecosystems (Shao, Wu, and Li 2021;Shao et al 2019;Shao, Zhang, and Wang 2017;Prins and Van Niekerk 2020). Finally, the irrigation infrastructure covered was only NIS.…”

Section: Discussionmentioning

confidence: 99%

Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine

Torre

Gao

Macinnis‐Ng

et al. 2021

Geo-spatial Information Science

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Paddy rice agriculture is practiced in both rain-fed and irrigated ecosystems in the Philippines. However, small farms are prevalent in the region, and current satellite-based mapping techniques do not distinguish between the two ecosystems at farm scales. This study developed an approach to rapidly map irrigated and rain-fed paddy rice in Iloilo, Philippines at 10 m resolutions using Google Earth Engine. This approach used an ensemble of classifiers based on time-series vegetation indices to produce dry and wet seasonal maps for the entire province. Results showed a predominance of rain-fed rice areas in both seasons, with irrigated rice making up only onefourth of the total rice area. The overall accuracy was achieved at 68% for the dry season and 75% for the wet season based on ground-acquired points and very high-resolution imagery. The two types of paddies were classified at accuracies up to 87%. Furthermore, the land cover maps showed a strong agreement with the municipal statistics. The resultant maps complement current official statistics and demonstrate the prowess of phenology-based mapping to create paddy inventories in a timely manner to inform food security and agricultural policies.

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Section: Discussionmentioning

confidence: 99%

Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine

Torre

Gao

Macinnis‐Ng

et al. 2021

Geo-spatial Information Science

View full text Add to dashboard Cite

show abstract

“…Datasets of broad sizes exist in several real data applications such as pattern recognition, data mining, signal processing, machine learning, text processing, image processing, and web content classification [1][2][3]. These datasets typically contain a significant range of hard-to-cope features.…”

Section: Introductionmentioning

confidence: 99%

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

et al. 2021

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Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.

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“…The LiDAR point cloud data obtained by MMS can be used to draw large-scale topographic maps and electronic maps, power-line inspection and so on. Fusing panoramic image data collected by MMS and LiDAR data, a true color point cloud threedimensional model can be obtained, which can be applied to road target detection, high-precision map making and urban three-dimensional scene display, et al (Shao and Cai 2018;Shao, Zhang, and Wang 2017;Shao et al 2016;Shao, Wu, and Li 2021). It becomes increasingly important since the emergence of some novel techniques, such as autonomous driving, urban brain, digital twin city, et al (Shao et al 2020;Li et al 2013;Shao and Li 2011;Shams et al 2018;Li et al 2016;Craciun et al 2014;Yoshimura et al 2016;Yi and Li 2007;Sester 2020;Yang and Wang 2016;Hollick, Helmholz, and Belton 2016;Ghouaiel and Lefèvre 2016;Sun et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Urban Geospatial Information Acquisition Mobile Mapping System based on close-range photogrammetry and IGS site calibration

Guo

Zhou

Zhao

et al. 2021

Geo-spatial Information Science

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The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measurement accuracy has always been a research hotspot in the industry. This paper proposes a position and attitude calibration method with error correction based on the combination of the feature point and feature surface. First, the initial value of the spatial position relationship between each sensor of MMS is obtained by close-range photogrammetry. Second, the optimal solution for error correction is calculated by feature points in global coordinates jointly measured with International GNSS Service (IGS) stations. Then, the final transformation parameters are solved by combining the initial values obtained originally, thereby realizing the rapid calibration of the MMS. Finally, it analyzed the RMSE of MMS point cloud after calibration, and the results demonstrate the feasibility of the calibration approach proposed by this method. Under the condition of a single measurement sensor accuracy is low, the plane and elevation absolute accuracy of the point cloud after calibration can reach 0.043 m and 0.072 m, respectively, and the relative accuracy is smaller than 0.02 m. It meets the precision requirements of data acquisition for MMS. It is of great significance for promoting the development of MMS technology and the application of some novel techniques in the future, such as autonomous driving, digital twin city, urban brain et al.

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Spatio-temporal-spectral observation model for urban remote sensing

Cited by 50 publications

References 35 publications

Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine

Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

Urban Geospatial Information Acquisition Mobile Mapping System based on close-range photogrammetry and IGS site calibration

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