Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery

Myint, Soe W.; Gober, Patricia; Brazel, Anthony J.; Grossman‐Clarke, Susanne; Weng, Qihao

doi:10.1016/j.rse.2010.12.017

Cited by 1,078 publications

(691 citation statements)

References 37 publications

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“…With such data sets, object-based classification approaches have several advantages compared to pixel-based approaches [30][31][32][33][34]. For example, using object-based image analysis (OBIA), the impact of noise is minimized.…”

Section: Introductionmentioning

confidence: 99%

“…All algorithms have advantages and disadvantages, and there is no perfect segmentation algorithm for defining object boundaries [44][45][46]. Many scientific studies rely on the Multiresolution Segmentation algorithm [9,30,34,37,[40][41][42][47][48][49][50]. This algorithm starts with one-pixel image segments, and merges neighboring segments together until a heterogeneity threshold is reached [51].…”

Section: Introductionmentioning

confidence: 99%

“…Only well-chosen segmentation parameters ensure good segmentation results. Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results [34,48,49]. Obviously, the manual approach is also highly subjective and therefore not always reproducible.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

et al. 2015

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Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the OPEN ACCESSRemote Sens. 2015, 7 14483proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km 2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ≈ 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

et al. 2015

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“…The key to OBC is to exhibit features such as image textural; spectral and spatial information to obtain an accurate analysis [14]. Object-based classification provided higher accuracy compared to urban class pixel-based classifications for a mixed urban-agricultural arid region of Arizona [15]- [16]. Previous research combined remote sensing data with spatial metrics for assessing and mapping urban sprawl, urban structures, sprawl direction, and urban growth pattern [17]- [18]- [19].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Analysis of Land Use and Land Cover Changes in Arid Region of Saudi Arabia

Albalawi¹

2018

GEOMATE

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Changes in land use/land cover (LU/LC) are a major factor driving global environmental change, especially in arid and semi-arid regions. As a result, expansion of agriculture and urbanization is occurring in ecologically fragile lands. This paper analyzed and assessed land use/land cover changes in an agricultural center Tabuk in Saudi Arabia, object-based classification technique (OBC) using Landsat images for seven years (1985, 1990, 1995, 2000, 2005, 2009, and 2015) were used for LU/LC analysis. The urban growth type for Tabuk was measured using Urban Growth Analysis Tool (UGAT) within a GIS. The study found an increase in urban areas from 48 km 2 in 1985 to 315 km 2 in 2015. A significant increase of 61% in agricultural land, from 1985 (112 km 2 ) to (577 km 2 ) in 2015. This expansion is occurring at the expense of the barren land with a loss of 768 km 2 . An overall classification accuracy ranged from 94% to 97% across all images. The urban growth type for Tabuk was determined as 72% to be of the extension-type over the study period, with urban areas expanding in a direction towards the northwest and west area. Assessing spatial and temporal LU/LC change has aided to understand the impacts of expanding agriculture and urban areas. The LU/LC maps could aid government planners and decision-makers to analyze and manage agricultural and urban growth.

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“…The approach allows use of multiple image elements, parameters and scales such as texture, shape and context, as opposed to pixel-based classification that solely relies on the pixel value. Overall, OBIA has been proven to produce more accurate classification results compared to pixel-based approaches using medium to high resolution imagery, producing improvements in classification accuracy ranging from 9-23% [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Mapping Net Stocked Plantation Area for Small-Scale Forests in New Zealand Using Integrated RapidEye and LiDAR Sensors

Xu¹,

Morgenroth²,

Manley³

2017

Forests

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Abstract:In New Zealand, approximately 70% of plantation forests are large-scale (over 1000 ha) with accurate resource description. In contrast, the remaining 30% of plantation forests are small-scale (less than 1000 ha). It is forecasted that these small-scale forests will supply nearly 40% of the national wood production in the next decade. However, in-depth description of these forests, especially those under 100 ha, is very limited. This research evaluates the use of remote sensing datasets to map and estimate the net stocked plantation area for small-scale forests. We compared a factorial combination of two classification approaches (Nearest Neighbour (NN), Classification and Regression Tree (CART)) and two remote sensing datasets (RapidEye, RapidEye plus LiDAR) for their ability to accurately classify planted forest area. CART with a combination of RapidEye and LiDAR metrics outperformed the other three combinations producing the highest accuracy for mapping forest plantations (user's accuracy = 90% and producer's accuracy = 88%). This method was further examined by comparing the mapped plantations with manually digitised plantations based on aerial photography. The mapping approach overestimated the plantation area by 3%. It was also found that forest patches exceeding 10 ha achieved higher conformance with the digitised areas. Overall, the mapping approach in this research provided a proof of concept for deriving forest area and mapping boundaries using remote sensing data, and is especially relevant for small-scale forests where limited information is currently available.

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Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery

Cited by 1,078 publications

References 37 publications

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

Spatio-Temporal Analysis of Land Use and Land Cover Changes in Arid Region of Saudi Arabia

Mapping Net Stocked Plantation Area for Small-Scale Forests in New Zealand Using Integrated RapidEye and LiDAR Sensors

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