Object-based classification using LiDAR-derived metrics and QuickBird imagery

Wang, An; Zhao, Shuhe; Zhou, Huakun; Luo, Yunxiao; Tan, Lei

doi:10.1109/eorsa.2012.6261161

Cited by 3 publications

(4 citation statements)

References 6 publications

(6 reference statements)

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“…Afterwards was applied a median filter with a kernel size of "3" to remove salt and pepper-noise, and used histogram normalization to stretch pixel values to the entire pixel value range for contrast enhancement [9].…”

Section: Data Pre-processingmentioning

confidence: 99%

“…Object-oriented techniques which combine the use of multispectral satellite data with other ancillary data, such as LIDAR data, are even more promising [7,8,9]. LIDAR (LIght Detection And Ranging) is an optical remote-sensing technique which uses laser light to produce clouds of points with three-dimensional coordinates.…”

Section: Introductionmentioning

confidence: 99%

“…Vegetation height can be obtained from LIDAR data, and LIDAR data have been explored by forest researchers to detect tree height, delineate single trees, define forest stand structure and estimate biomass and wood volume [10,11]. LIDAR data have also been used in combination with multispectral or panchromatic imagery for the creation of land use maps [7,9].…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection and agronomic characterization of olive groves using high-resolution imagery and LIDAR data

et al. 2014

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The Common Agricultural Policy of the European Union grants subsidies for olive production. Areas of intensified olive farming will be of major importance for the increasing demand for oil production of the next decades, and countries with a high ratio of intensively and super-intensively managed olive groves will be more competitive than others, since they are able to reduce production costs.It can be estimated that about 25-40% of the Sicilian oliviculture must be defined as "marginal". Modern olive cultivation systems, which permit the mechanization of pruning and harvest operations, are limited.Agronomists, landscape planners, policy decision-makers and other professionals have a growing need for accurate and cost-effective information on land use in general and agronomic parameters in the particular. The availability of high spatial resolution imagery has enabled researchers to propose analysis tools on agricultural parcel and tree level.In our study, we test the performance of WorldView-2 imagery relative to the detection of olive groves and the delineation of olive tree crowns, using an object-oriented approach of image classification in combined use with LIDAR data.We selected two sites, which differ in their environmental conditions and in their agronomic parameters of olive grove cultivation. The main advantage of the proposed methodology is the low necessary quantity of data input and its automatibility. However, it should be applied in other study areas to test if the good results of accuracy assessment can be confirmed.Data extracted by the proposed methodology can be used as input data for decision-making support systems for olive grove management.

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Section: Data Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic detection and agronomic characterization of olive groves using high-resolution imagery and LIDAR data

et al. 2014

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“…Low-posting-density LiDAR maps are largely limited to applications of terrestrial topographic mapping [Hodgson and Bresnahan, 2004]. Wang et al [2012] combine QuickBird imagery with LiDAR-derived metrics for an object-based classification of vegetation, roads and buildings.…”

Section: Introductionmentioning

confidence: 99%

Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping

Sturari

Frontoni

Pierdicca

et al. 2017

European Journal of Remote Sensing

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The combination of elevation data together with multispectral high-resolution images is a new methodology for obtaining land use/land cover classification. It represents a step forward for both the accuracy and automation of LULC applications and allows users to setup thematic assignments through rules based on feature attributes and human expert interpretation of land usage. The synergy between different types of information means that LiDAR can give new hints at both the segmentation and hybrid classification steps, leading to a joint use of multispectral, spatial and elevation data. The output is a thematic map characterized by a custom-designed legend that is able to discriminate between land cover classes with similar spectral characteristics (level 3 of the CLC legend). Experimental results from a hilly farmland area with some urban structures (Musone river basin, Ancona, Italy) are used to highlight how the proposed methodology enhances land cover classification in heterogeneous environments.

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A Comparative Land-Cover Classification Feature Study of Learning Algorithms: DBM, PCA, and RF Using Multispectral LiDAR Data

Pan

Guan

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Object-based classification using LiDAR-derived metrics and QuickBird imagery

Cited by 3 publications

References 6 publications

Automatic detection and agronomic characterization of olive groves using high-resolution imagery and LIDAR data

Automatic detection and agronomic characterization of olive groves using high-resolution imagery and LIDAR data

Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping

A Comparative Land-Cover Classification Feature Study of Learning Algorithms: DBM, PCA, and RF Using Multispectral LiDAR Data

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