A study on the use of UAV images to improve the separation accuracy of agricultural land areas

Malamiri, Hamid Reza Ghafarian; Aliabad, Fahime Arabi; Shojaei, Saeed; Mortaz, Morad; Band, Shahab S.

doi:10.1016/j.compag.2021.106079

Cited by 25 publications

(11 citation statements)

References 27 publications

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“…The DT method uses interrelated classifiers which perform each part of the classification process and do not operate alone. The DT is a representation of branches and nodes, with each node leading to a set of possible answers (Lennon, 2002). In this method, the optimal branch structure with the lowest error rate and the minimum number of nodes should be specified, and the class sharing and the number of used branches and layers should be considered.…”

Section: Decision Tree Methodsmentioning

confidence: 99%

“…Currently, the use of multi-source and multispectral data leads to more advanced identifications of the state of infrastructure (Schnebele et al, 2015). Research has shown that UAV is a low-cost option to provide surveillance images (Malamiri et al, 2021). In this regard, many studies using remote sensing have examined the condition of unpaved roads.…”

Section: The Separation Of the Unpaved Roads And Prioritization Of Pa...mentioning

confidence: 99%

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The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images

Mansourmoghaddam

Malamiri

Aliabad

et al. 2022

Air, Soil and Water Research

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Prioritization of pathways to perform asphalt pavement operations has always been one of the most important concerns for municipalities, for which, currently there is no specific planning and pattern. In the present study, using (Unmanned Aerial Vehicle) UAV images, a land cover map of the case study was prepared. For this purpose, the accuracy of various object-based classification methods including the Bayes method, the Support Vector Machine (SVM), the K nearest neighbor (KNN), the Decision tree (DT), and the Random tree (RT) was investigated. Findings of the study showed that by increasing heterogeneity in the composition of the studied phenomenon in the image, different classification algorithms offer results different from each other. The obtained results of the accuracy evaluation of classification methods indicate that the SVM method with 80% kappa coefficient and 89% overall accuracy had the best performance compared to other methods. As a result, built-up land covers, bare land, vegetation cover, and paved roads were separated using this method. Then, the exact boundary of pathways was prepared using Google Earth images, and then, using the land-use map prepared from the case study, the roads were divided into two categories: paved and unpaved. To determine the prioritization of unpaved roads for applying asphalt, the proportion of built-up lands (BUL) to bare (non-built-up) lands (BL) was used in each path. Based on the obtained results, 1% of the roads in the case study was placed on a very high level of asphalt, and then 9%, 3%, 49%, 38%, were placed on a high priority to low priority, respectively.

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Section: Decision Tree Methodsmentioning

confidence: 99%

Section: The Separation Of the Unpaved Roads And Prioritization Of Pa...mentioning

confidence: 99%

The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images

Mansourmoghaddam

Malamiri

Aliabad

et al. 2022

Air, Soil and Water Research

Self Cite

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“…Adaptive resonance theory (ART)-based networks are a family of neural networks that have been used in spatial interaction flows, crop classification, and land-use change applications [134][135][136]. ART-based networks are supervised, self-organizing, and selfstabilizing neural networks that can learn fast in nonstationary environments [137].…”

Section: Adaptive Resonance Theory Networkmentioning

confidence: 99%

Machine Learning of Spatial Data

Nikparvar

Thill

2021

IJGI

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Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue that they offer the most promising prospects for the future of spatial machine learning.

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Section: Introductionmentioning

confidence: 99%

“…To improve the efficiency of the investigation, four types of remote sensing data are used to examine the spatial distribution of non-grain production: (1) Data from the 3rd National Land Resource Survey, derived from visual interpretation of satellite images from Gaofen-2, have been used to obtain non-grain production information [16]. (2) Operational Land Imager data from the Landsat 8 satellite with a spatial resolution of 30 m, combined with UAV images, were used to separate the grassland and trees in the cultivated land [23]. (3) Aerial photos with a spatial resolution of less than 1 m have been used to visually interpret the non-grain production in cultivated land and to analyze the type and spatial distribution of non-grain production, combined with land use planning, annual land use survey data, and soil maps [24].…”

Section: Introductionmentioning

confidence: 99%

Identifying Peach Trees in Cultivated Land Using U-Net Algorithm

Zhang

2022

Land

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Non-grain production has emerged as a potential threat to grain production capacity and security in China. Agricultural products with higher economic returns are beginning to replace traditional grain crops, which have relatively low economic returns on a large scale. In this study, we proposed and verified an identification method utilizing an unmanned aerial vehicle and a U-net algorithm to distinguish peach trees in cultivated land; the overall accuracy for verification and prediction were 0.90 and 0.92, respectively. Additionally, a non-grain production index was developed to assess the degree of non-grain production in target plots. The index was 76.90% and 91.38% in the projected plots, representing a high degree of non-grain production. This combination of an identification method and non-grain production index could provide efficient tools for agricultural management to inspect peach trees in cultivated land, thus replacing field measurements to achieve significant labor savings. Furthermore, this method can provide a reference for creating high-standard farmland, sustainable development of cultivated land, and policymaking.

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A study on the use of UAV images to improve the separation accuracy of agricultural land areas

Cited by 25 publications

References 27 publications

The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images

The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images

Machine Learning of Spatial Data

Identifying Peach Trees in Cultivated Land Using U-Net Algorithm

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