A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images

Ziaei, Zahra; Pradhan, Biswajeet; Mansor, Shattri

doi:10.1080/10106049.2013.819039

Cited by 26 publications

(15 citation statements)

References 36 publications

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“…Nevertheless, accelerated urbanization can lead to, not only deterioration of the quality of life, but also to environmental degradation especially in developing countries with a few coping strategies [3][4][5]. Cropland conversion, land use competition and wetlands alteration are among the aftermaths linked to rapid urbanization [6,7]. In the urbanization hotspots of Sub-Saharan Africa, the Indian sub-continent and Latin America, a population boom, a slums' development and accelerated land cover conversion in urban fringe zones are key characteristics [8].…”

Section: Introductionmentioning

confidence: 99%

WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

2019

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The emergence of high-resolution satellite data, such as WorldView-2, has opened the opportunity for urban land cover mapping at fine resolution. However, it is not straightforward to map detailed urban land cover and to detect urban deprived areas, such as informal settlements, in complex urban environments based merely on high-resolution spectral features. Thus, approaches integrating hierarchical segmentation and rule-based classification strategies can play a crucial role in producing high quality urban land cover maps. This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized by a subtropical highland climate. A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral indices, gray level co-occurrence matrix (GLCM) texture measures and a digital terrain model (DTM). In the initial classification, confusion existed among the informal settlements, the high- and low-density built-up areas, as well as between the upland and lowland agriculture. To improve the classification accuracy, a framework based on a geometric ruleset and two newly defined indices (urban density and greenness density indices) were developed. The novel framework resulted in an overall classification accuracy at 85.36% with a kappa coefficient at 0.82. The confusion between high- and low-density built-up areas significantly decreased, while informal settlements were successfully extracted with the producer and user’s accuracies at 77% and 90% respectively. It was revealed that the integration of an object-based SVM classification of WorldView-2 feature sets and DTM with the geometric ruleset and urban density and greenness indices resulted in better class separability, thus higher classification accuracies in complex urban environments.

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Section: Introductionmentioning

confidence: 99%

WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

2019

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“…In some studies, rule-based via automatic methods are applied. For example, (Ren, Zargham et al 2006, Zhang and Zhu 2011, Jumlesha, Babu et al 2012, Arvor, Saint-Geours et al 2013, Ziaei, Pradhan et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Automatic Building Extraction Using a Decision Tree Object-Based Classification on Joint Use of Aerial and Lidar Data

Nejad

Pahlavani

Bigdeli

2019

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

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Abstract. Updating digital maps is a challenging task that has been considered for many years and the requirement of up-to-date urban maps is universal. One of the main procedures used in updating digital maps and spatial databases is building extraction which is an active research topic in remote sensing and object-based image analysis (OBIA). Since in building extraction field a full automatic system is not yet operational and cannot be implemented in a single step, experts are used to define classification rules based on a complex and subjective “trial-and-error” process. In this paper, a decision tree classification method called, C4.5, was adopted to construct an automatic model for building extraction based on the remote sensing data. In this method, a set of rules was derived automatically then a rule-based classification is applied to the remote sensing data include aerial and lidar images. The results of experiments showed that the obtained rules have exceptional predictive performance.

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“…In recent years, the OBIA technique has continued to be studied in building extraction. Ziaei et al [15] present a comparison between three object-based models for urban feature classification from WorldView-2 images. Based on a Markov random field model, Grinias et al [16] present a novel segmentation algorithm for building and road detection.…”

Section: Obia Approachmentioning

confidence: 99%

Object-Based Features for House Detection from RGB High-Resolution Images

Chen

2018

Remote Sensing

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Automatic building extraction from satellite images, an open research topic in remote sensing, continues to represent a challenge and has received substantial attention for decades. This paper presents an object-based and machine learning-based approach for automatic house detection from RGB high-resolution images. The images are first segmented by an algorithm combing a thresholding watershed transformation and hierarchical merging, and then shadows and vegetation are eliminated from the initial segmented regions to generate building candidates. Subsequently, the candidate regions are subjected to feature extraction to generate training data. In order to capture the characteristics of house regions well, we propose two kinds of new features, namely edge regularity indices (ERI) and shadow line indices (SLI). Finally, three classifiers, namely AdaBoost, random forests, and Support Vector Machine (SVM), are employed to identify houses from test images and quality assessments are conducted. The experiments show that our method is effective and applicable for house identification. The proposed ERI and SLI features can improve the precision and recall by 5.6% and 11.2%, respectively.

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A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images

Cited by 26 publications

References 36 publications

WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

Automatic Building Extraction Using a Decision Tree Object-Based Classification on Joint Use of Aerial and Lidar Data

Object-Based Features for House Detection from RGB High-Resolution Images

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