Rapid, man-made object morphological segmentation for aerial images using a multi-scaled, geometric image analysis

Wang, Wei; Yang, Xin

doi:10.1016/j.imavis.2009.10.002

Cited by 16 publications

(9 citation statements)

References 27 publications

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“…Even at moderate scale distortion, classification error percentage (CEP) is scarcely ever zero [1], [2], [4]. At maximal scale distortion, CEP rises up to a few percent [12].…”

Section: Related Workmentioning

confidence: 99%

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Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

Romanuke

2015

Information Technology and Management Science

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-The research is focused on optimising two-layer perceptron for generalised scaled object classification problem. The optimisation criterion is minimisation of inaccuracy. The inaccuracy depends on training parameters and hidden layer neuron number. After its statistics is accumulated, minimisation is executed by a numerical search. Perceptron is optimised additionally by extra training. As it is done, the classification error percentage does not exceed 3 % in case of the worst scale distortion.

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“…Even at moderate scale distortion, classification error percentage (CEP) is scarcely ever zero [1], [2], [4]. At maximal scale distortion, CEP rises up to a few percent [12].…”

Section: Related Workmentioning

confidence: 99%

“…However, threedimensional objects also occur scaled [3]. Scaling of multidimensional objects can be comprehended as scaled coloured images with metadata [4].…”

Section: Introductionmentioning

confidence: 99%

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

Romanuke

2015

Information Technology and Management Science

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“…Thresholding techniques range from simple to intricate and from fast to computationally intensive. They may include methods that employ artificial intelligence, neural networks and genetic algorithms (Wei and Xin, 2010;Davies, 2012). Thresholding can be very complex, depending on methods used to choose the thresholds and on whether the thresholding is applied to the entire image or to selected regions.…”

Section: Segmenting An Image Using Thresholdingmentioning

confidence: 99%

Breaking free from the matrix: Segmentation of fossil images

Honeycutt

Plotnick

Kenig

2014

Palaeontologia Electronica

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The traditional method for separating a fossil from its matrix is to physically remove the matrix, risking damage to the fossil. Non-destructive technologies, such as computed tomography (CT) scanning, digitally remove the matrix. However, these techniques have multiple disadvantages: they are time consuming, involve highly skilled preparators or technicians, or require expensive equipment. Image segmentation, coupled with wavelet denoising, provides a rapid, inexpensive, and non-destructive way to digitally separate matrix from fossil in standard digital camera images. This method uses off-the-shelf software and produces results that can then be input into software for morphometric analysis or used to speed up more traditional segmentation techniques. A comparison of alternative methods for image segmentation and wavelet denoising indicate that Otsu's method and other global thresholding techniques perform best for this purpose. This approach is illustrated here with images of Eucalyptocrinites holdfasts. The resulting images are used for morphometrics. These methods have great potential for studies that utilize the rapidly growing resource of online museum digital image catalogs.

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“…These approaches exhibit different features, models, and classifiers to accomplish the classification task. Several texture features have been used as an input to the classification stage, such as Gabor filter (Baik et al, 2004), fractal dimension and coefficient of variation (Solka et al, 1998), and Non Subsampled Contourlet Transform NSCT (Wei et al, 2010). The pixel color components have been used directly as the input to the classifier (Mokhtarzade et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

A New Object Based Method for Automated Extraction of Urban Objects From Airborne Sensors Data

Moussa

El‐Sheimy

2012

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

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ABSTRACT:The classification of urban objects such as buildings, trees and roads from airborne sensors data is an essential step in numerous mapping and modelling applications. The automation of this step is greatly needed as the manual processing is costly and time consuming. The increasing availability of airborne sensors data such as aerial imagery and LIDAR data offers new opportunities to develop more robust approaches for automatic classification. These approaches should integrate these data sources that have different characteristics to exceed the accuracy achieved using any individual data source. The proposed approach presented in this paper fuses the aerial images data with single return LIDAR data to extract buildings and trees for an urban area. Object based analysis is adopted to segment the entire DSM data into objects based on height variation. These objects are preliminarily classified into buildings, trees, and ground. This primary classification is used to compute the height to ground for each object to help improve the accuracy of the second phase of classification. The overlapping perspective aerial images are used to build an ortho-photo to derive a vegetation index value for each object. The second phase of classification is performed based on the height to ground and the vegetation index of each object. The proposed approach has been tested using three areas in the centre of the city of Vaihingen provided by ISPRS test project on urban classification and 3D building reconstruction. These areas have historic buildings having rather complex shapes, few high-rising residential buildings that are surrounded by trees, and a purely residential area with small detached houses. The results of the proposed approach are presented based on a reference solution for evaluation purposes. The classification evaluation exhibits highly successful classification results of buildings class. The proposed approach follows the exact boundary of trees based on LIDAR data which provide above average classification results for the trees when compared to the assumed ideal circular shaped trees in the reference data.

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Rapid, man-made object morphological segmentation for aerial images using a multi-scaled, geometric image analysis

Cited by 16 publications

References 27 publications

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

Breaking free from the matrix: Segmentation of fossil images

A New Object Based Method for Automated Extraction of Urban Objects From Airborne Sensors Data

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