Object-oriented mapping of urban trees using Random Forest classifiers

Puissant, A; Rougier, Simon; Stumpf, André

doi:10.1016/j.jag.2013.07.002

Cited by 158 publications

(102 citation statements)

References 41 publications

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“…In recent years, the random forest (RF) algorithm has become one of the most popular methods for classification tasks. RF has been used in many applications, such as mapping application using object-based methods (Stumpf and Kerle 2011;Puissant, Rougier, and Stumpf 2014), high-resolution image processing (Immitzer, Atzberger, and Koukal 2012), hyperspectral image processing (Ham et al 2005;Amini, Homayouni, and Safari 2014), and many other applications.…”

Section: Open Accessmentioning

confidence: 99%

“…The novelty of this approach is in the automatic determination of the weight parameter, which is one of the segmentation parameters, for the data with a high number of input features (bands). In previous studies (Hay et al 2005;Puissant, Rougier, and Stumpf 2014), there is less effort for determination of the weight parameter because of less number of input features. However, using a large number of input features, such as hyperspectral data, requires an automatic method for assigning a weight for each input feature (band).…”

Section: Open Accessmentioning

confidence: 99%

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Object-based classification of hyperspectral data using Random Forest algorithm

Amini

Homayouni

Safari

et al. 2018

Geo-spatial Information Science

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This paper presents a new framework for object-based classification of high-resolution hyperspectral data. This multi-step framework is based on multi-resolution segmentation (MRS) and Random Forest classifier (RFC) algorithms. The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images. Given the high number of input features, an automatic method is needed for estimation of this parameter. Moreover, we used the Variable Importance (VI), one of the outputs of the RFC, to determine the importance of each image band. Then, based on this parameter and other required parameters, the image is segmented into some homogenous regions. Finally, the RFC is carried out based on the characteristics of segments for converting them into meaningful objects. The proposed method, as well as, the conventional pixel-based RFC and Support Vector Machine (SVM) method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics. These data were acquired by the HyMap, the Airborne Prism Experiment (APEX), and the Compact Airborne Spectrographic Imager (CASI) hyperspectral sensors. The experimental results show that the proposed method is more consistent for land cover mapping in various areas. The overall classification accuracy (OA), obtained by the proposed method was 95.48, 86.57, and 84.29% for the HyMap, the APEX, and the CASI datasets, respectively. Moreover, this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75% higher than the conventional RFC and SVM classifiers, respectively.

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Section: Open Accessmentioning

confidence: 99%

Section: Open Accessmentioning

confidence: 99%

Object-based classification of hyperspectral data using Random Forest algorithm

Amini

Homayouni

Safari

et al. 2018

Geo-spatial Information Science

View full text Add to dashboard Cite

show abstract

“…RF works on the robust idea of combining outputs from more than one classifier to improve its accuracy. RF does not overfit, is less sensitive to noise, relatively quick compared to other classification methods like boosting [71] and better adapted for large datasets [73]. Compared to larger number of parameters required for other machine learning methods, the RF classifier has only two parameters: the number of trees (T) to grow the whole forest and the number of randomly selected variables (M) chosen as each split.…”

Section: Image Classificationmentioning

confidence: 99%

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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“…We chose this algorithm because it gives good performance for the retrieval of land cover/use classes [38]. MRIS is an algorithm of segmentation by "region growing", where a scale parameter is used as the maximum heterogeneity threshold during the fusion process [11].…”

Section: Segmentation and Feature Computationmentioning

confidence: 99%

Multi-Scale Analysis of Very High Resolution Satellite Images Using Unsupervised Techniques

2017

Self Cite

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Abstract:This article is concerned with the use of unsupervised methods to process very high resolution satellite images with minimal or little human intervention. In a context where more and more complex and very high resolution satellite images are available, it has become increasingly difficult to propose learning sets for supervised algorithms to process such data and even more complicated to process them manually. Within this context, in this article we propose a fully unsupervised step by step method to process very high resolution images, making it possible to link clusters to the land cover classes of interest. For each step, we discuss the various challenges and state of the art algorithms to make the full process as efficient as possible. In particular, one of the main contributions of this article comes in the form of a multi-scale analysis clustering algorithm that we use during the processing of the image segments. Our proposed methods are tested on a very high resolution image (Pléiades) of the urban area around the French city of Strasbourg and show relevant results at each step of the process.

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Object-oriented mapping of urban trees using Random Forest classifiers

Cited by 158 publications

References 41 publications

Object-based classification of hyperspectral data using Random Forest algorithm

Object-based classification of hyperspectral data using Random Forest algorithm

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

Multi-Scale Analysis of Very High Resolution Satellite Images Using Unsupervised Techniques

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