Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests

Guo, Li; Chehata, Nesrine; Mallet, Clément; Boukir, Samia

doi:10.1016/j.isprsjprs.2010.08.007

Cited by 421 publications

(235 citation statements)

References 26 publications

(32 reference statements)

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“…The next step in the process was to choose a suitable image classification method. We used the Random Forests (RF) classification in this study, which has demonstrated excellent performance for the analysis of different remote sensing datasets [68][69][70]. Random Forest, first developed by Breiman (2001) [71], is an ensemble method for supervised classification and regression based on classification and regression trees (CART).…”

Section: Image Classificationmentioning

confidence: 99%

“…Two methods are used to calculate M in Random Forest, one-third or square root of the number of input variables [70]. Only two-third of random samples were chosen as training set and remaining one-third samples, called Out-of-Bag (OOB) samples, are used as test samples to compute classification accuracy [69]. This OOB accuracy is generally plotted against the increment of number of trees to determine appropriate number of trees.…”

Section: Image Classificationmentioning

confidence: 99%

“…Thus, the number of trees (T) was selected to 100. M was chosen to be square root of the number of input variables [69]. Random Forest classification performed well in the complex landscape of Lake Kivu region with its heterogeneous environment.…”

Section: Classification Performancementioning

confidence: 99%

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Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Basnet

Vodacek

2015

Remote Sensing

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Tracking land surface dynamics over cloud prone areas with complex mountainous terrain is an important challenge facing the Earth Science community. One such region is the Lake Kivu region in Central Africa. We developed a processing chain to systematically monitor the spatio-temporal land use/land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988-2001 and 2001-2011 period was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. These multi-temporal datasets will be a valuable baseline for land use managers in the region OPEN ACCESSRemote Sens. 2015, 7 6684 interested in developing ecologically sustainable land management strategies and measuring the impacts of biodiversity conservation efforts.

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Section: Image Classificationmentioning

confidence: 99%

Section: Image Classificationmentioning

confidence: 99%

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Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Basnet

Vodacek

2015

Remote Sensing

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“…In this regard, the straightforward solution consists in selecting a standard approach for supervised classification, e.g. a Support Vector Machine classifier Lodha et al, 2006), a Random Forest classifier (Chehata et al, 2009;Guo et al, 2011;Steinsiek et al, 2017), an AdaBoost(-like) classifier (Lodha et al, 2007;Guo et al, 2015) or a Bayesian Discriminant Analysis classifier (Khoshelham and Oude Elberink, 2012). However, as these classifiers treat each point of the point cloud individually, they do not take into account a spatial regularity of the derived labeling, i.e.…”

Section: Classificationmentioning

confidence: 99%

Using Multi-Scale Features for the 3d Semantic Labeling Of Airborne Laser Scanning Data

Blomley

Weinmann

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, we present a novel framework for the semantic labeling of airborne laser scanning data on a per-point basis. Our framework uses collections of spherical and cylindrical neighborhoods for deriving a multi-scale representation for each point of the point cloud. Additionally, spatial bins are used to approximate the topography of the considered scene and thus obtain normalized heights. As the derived features are related with different units and a different range of values, they are first normalized and then provided as input to a standard Random Forest classifier. To demonstrate the performance of our framework, we present the results achieved on two commonly used benchmark datasets, namely the Vaihingen Dataset and the GML Dataset A, and we compare the results to the ones presented in related investigations. The derived results clearly reveal that our framework excells in classifying the different classes in terms of pointwise classification and thus also represents a significant achievement for a subsequent spatial regularization.

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“…With aerial data acquisition, urban classification typically further diversifies -not only building outlines are extracted (Ortner et al, 2007), but typically the scenery is divided into vegetation, ground and buildings. Besides pure 2D image segmentation, state-of-the-art is to use 3D point cloud information obtained from dense image matching (Haala and Rothermel, 2015) or LiDAR (Guo et al, 2011). Data obtained by LiDAR systems can either stem from airborne laser scanning (ALS) or terrestrial -either static (TLS) or mobile (MLS).…”

Section: Urban Classificationmentioning

confidence: 99%

Processing of Crawled Urban Imagery for Building Use Classification

Tutzauer¹,

Haala²

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Recent years have shown a shift from pure geometric 3D city models to data with semantics. This is induced by new applications (e.g. Virtual/Augmented Reality) and also a requirement for concepts like Smart Cities. However, essential urban semantic data like building use categories is often not available. We present a first step in bridging this gap by proposing a pipeline to use crawled urban imagery and link it with ground truth cadastral data as an input for automatic building use classification. We aim to extract this city-relevant semantic information automatically from Street View (SV) imagery. Convolutional Neural Networks (CNNs) proved to be extremely successful for image interpretation, however, require a huge amount of training data. Main contribution of the paper is the automatic provision of such training datasets by linking semantic information as already available from databases provided from national mapping agencies or city administrations to the corresponding façade images extracted from SV. Finally, we present first investigations with a CNN and an alternative classifier as a proof of concept.

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Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests

Cited by 421 publications

References 26 publications

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Using Multi-Scale Features for the 3d Semantic Labeling Of Airborne Laser Scanning Data

Processing of Crawled Urban Imagery for Building Use Classification

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