Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data

Ma, Lei; Cheng, Liang; Han, Wenquan; Zhong, Liguo

doi:10.1117/1.jrs.8.083673

Cited by 30 publications

(7 citation statements)

References 40 publications

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“…Image segmentation processes for the delineation of agriculture from Unmanned Aerial Vehicle (UAV) images have been in operational use for several years, e.g., for precision agriculture [25,26]. UAV images are usually different to other images (typically only RGB bands, very high spatial resolution, radiometric differences).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers

Blaschke

et al. 2017

IJGI

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178

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Abstract:The increased feature space available in object-based classification environments (e.g., extended spectral feature sets per object, shape properties, or textural features) has a high potential of improving classifications. However, the availability of a large number of derived features per segmented object can also lead to a time-consuming and subjective process of optimizing the feature subset. The objectives of this study are to evaluate the effect of the advanced feature selection methods of popular supervised classifiers (Support Vector Machines (SVM) and Random Forest (RF)) for the example of object-based mapping of an agricultural area using Unmanned Aerial Vehicle (UAV) imagery, in order to optimize their usage for object-based agriculture pattern recognition tasks. In this study, several advanced feature selection methods were divided into both types of classifiers (SVM and RF) to conduct further evaluations using five feature-importance-evaluation methods and three feature-subset-evaluation methods. A visualization method was used to measure the change pattern of mean classification accuracy with the increase of features used, and a two-tailed t-test was used to determine the difference between two population means for both repeated ten classification accuracies. This study mainly contribute to the uncertainty analysis of feature selection for object-based classification instead of the per-pixel method. The results highlight that the RF classifier is relatively insensitive to the number of input features, even for a small training set size, whereby a negative impact of feature set size on the classification accuracy of the SVM classifier was observed. Overall, the SVM Recursive Feature Elimination (SVM-RFE) seems to be an appropriate method for both groups of classifiers, while the Correlation-based Feature Selection (CFS) is the best feature-subset-evaluation method. Most importantly, this study verified that feature selection for both classifiers is crucial for the evolving field of Object-based Image Analysis (OBIA): It is highly advisable for feature selection to be performed before object-based classification, even though an adverse impact could sometimes be observed from the wrapper methods.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers

Blaschke

et al. 2017

IJGI

Self Cite

178

117

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“…For instance, the fixed-wing UAV cannot make abrupt directional changes, and its turn radius should be larger than a certain threshold to ensure safety. 3 This can be achieved by designing the UAV's turning curves as the Dubin's path [64], [65]. In this study, we consider multirotor UAVs that can make sharp turns with arbitrary turn radius, which will significantly increase the flexibility of the path design.…”

Section: A Problem Description and Assumptionsmentioning

confidence: 99%

“…The attracting features of UAVs including flexibility, maneuverability, low cost and high mobility make them widely applicable. Example UAV applications include environmental monitoring [1], precision agriculture [2], land assessment [3] and emergency response [4], to name a few. To realize these applications, one of the key steps is path planning, which aims to design the path for the UAV such that, by following this path, the UAV will successfully complete the mission while satisfying mission-specific requirements (e.g., complete the mission with the minimum time).…”

Section: Introductionmentioning

confidence: 99%

Path Planning for UAV to Cover Multiple Separated Convex Polygonal Regions

2020

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In many unmanned aerial vehicle (UAV) applications such as land assessment, search and rescue, and precision agriculture, UAVs are often required to survey multiple spatially distributed regions. To perform these applications, one of the key steps is to plan the path for the UAV to quickly cover all regions. The new path planning problem explored here, which we call the TSP-CPP problem, can be viewed as an integration of the traveling salesman problem (TSP) and the coverage path planning (CPP) problem, which has not been well studied in the literature. In this paper, we conduct a systematic investigation on the TSP-CPP problem. In particular, we first provide a mixed integer programming formulation for this new problem, and then introduce a CPP method for covering a single convex polygonal region. Based on this method, we then develop two approaches to solve the TSP-CPP problem, including 1) a dynamic programming-based exact approach that can find the (near) optimal tour, and 2) a heuristic approach that can generate high-quality tours very efficiently. Through comprehensive theoretical analyses and simulation studies, we demonstrate the optimality and efficiency of the proposed approaches. INDEX TERMS Path planning, traveling salesman problem, coverage path planning, multiple convex polygonal regions, unmanned aerial vehicle, optimal path, heuristic algorithm.

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“…The application of HSROIs provides favorable conditions for the updating and application of GIS data and is of great significance for map updating, image matching, and target detection. 2,3 However, previous studies pay more attention to the information extraction of diverse land-cover objects (e.g., building, 4-8 road, [9][10][11][12][13] vegetation, [14][15][16][17] and cultivated land [18][19][20][21], and fewer studies focus on the extraction of functional zones. Functional zones are spatially aggregated by different land-cover objects, and their categories are semantically abstracted from land use functions, 22 e.g., commercial zones, industrial zones, residential districts, shanty towns, campuses, and parks.…”

Section: Introductionmentioning

confidence: 99%

Residential land extraction from high spatial resolution optical images using multifeature hierarchical method

Zhang

Liu

2019

J. Appl. Rem. Sens.

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Residential land extraction from high spatial resolution optical images using multifeature hierarchical method,"Abstract. Residential land (RL), as a typical kind of urban functional zone, plays an important role in urban planning and land census. Recent years have witnessed frequent changes in RL via the process of urbanization. The extraction of RL from high spatial resolution optical images can reflect the status quo of land use/land cover to a certain extent, which is of great significance to land census and urban planning. We adopt a scene classification strategy to extract RL and mainly focus on the extraction of four common types of RL in China: old-style village, lowdensity high-rise, medium-density low-rise, and low-density low-rise. We design a multifeature hierarchical (MFH) algorithm for RL extraction. First, RL is extracted based on the gray level concurrence matrix and a fuzzy classification algorithm. Then an improved bag-of-visual-words algorithm is introduced to further realize the extraction of RL. The effectiveness of our proposed method is analyzed with a sample dataset and large images. We also analyze the separability among different kinds of RL. We compare the experimental results with those of three other algorithms, and the results demonstrate that the MFH algorithm performs better in terms of the accuracy and efficiency of the RL extraction. The results can provide services for land surveying and urban planning, and the technological processes and experimental design in the algorithm can provide a reference for the research in related fields. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data

Cited by 30 publications

References 40 publications

Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers

Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers

Path Planning for UAV to Cover Multiple Separated Convex Polygonal Regions

Residential land extraction from high spatial resolution optical images using multifeature hierarchical method

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