Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

Shahi, Kaveh; Shafri, Helmi Zulhaidi Mohd; Hamedianfar, Alireza

doi:10.1080/10106049.2016.1213888

Cited by 19 publications

(9 citation statements)

References 48 publications

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“…It is a nonlinear metric derived from Pearson's chisquared test statistic, 22 and it creates a predictive model that minimizes the misclassifications. 32 The CHI 21 between two spectral signatures…”

Section: City Block Distancementioning

confidence: 99%

“…23,26,27 The chosen hybrid spectral similarity measures are SID-SAM, 28 SID-SCA, 23 SID-CBD, 25 SID-EUD, 25 CHI-SAM, 25 CHI-SCM, JMD-CHI, 25 and SID-CHI. 29 The outcomes of the classifications are summarized using the confusion matrices and evaluated for overall accuracy (OA), [30][31][32][33][34][35] 38 coefficient of determination (R 2 ), mean absolute error (MAE), and root mean squared error (RMSE). The results revealed the multiclass classification capability of hybrid SSM where the SID-SCA performed well on hyperspectral image data (VNIR) of paper samples compared with other SSM.…”

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confidence: 99%

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Classification of forensic hyperspectral paper data using hybrid spectral similarity algorithms

Deepthi

Devassy

George

et al. 2022

Journal of Chemometrics

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Document forgeries that involve modification of the materials used, such as ink and paper, provide evidence of any malpractices being performed. Forensic specialists use different techniques to identify and classify these samples; however, the most preferred method is to use nondestructive techniques to avoid any potential damage to the original specimen under investigation. Hyperspectral imaging has already been explored in several application domains and used as a powerful method in forensic investigations to extract information about the materials under examination. To precisely classify the material information and utilize the hyperspectral imaging technique's potential, we probed the potential of some hybrid spectral similarity measures to classify different commonly used paper samples. A comparison of these methods is quantitatively presented in this article.

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Section: City Block Distancementioning

confidence: 99%

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confidence: 99%

Classification of forensic hyperspectral paper data using hybrid spectral similarity algorithms

Deepthi

Devassy

George

et al. 2022

Journal of Chemometrics

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“…FS, which is regarded as a crucial task that influences the GEOBIA classification accuracy, specifies the most relevant features to increase the effectiveness of the adopted classification approach and expedites the processing time by minimizing irrelevant or redundant features [46]. Various FS algorithms have been incorporated with the GEOBIA approach in various applications, and these methods include RF [47,48], SVM [47], ant colony optimization (ACO) [49,50], artificial bee colony [51], hybrid particle swarm optimization [52], correlation-based FS (CFS) [49,53], and chi-square [54]. Ridha and Pradhan [49] applied three FS methods, namely, CFS, RF, and ACO, to discriminate several types of landslides from LiDAR data.…”

Section: Related Studiesmentioning

confidence: 99%

Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification

et al. 2020

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Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, detailed mapping of heterogeneous urban landscapes is extremely challenging because of the spectral similarity between classes. In this study, adaptive hierarchical image segmentation optimization, multilevel feature selection, and multiscale (MS) supervised machine learning (ML) models were integrated to accurately generate detailed maps for heterogeneous urban areas from the fusion of the UHSR orthomosaic and digital surface model (DSM). The integrated approach commenced through a preliminary MS image segmentation parameter selection, followed by the application of three supervised ML models, namely, random forest (RF), support vector machine (SVM), and decision tree (DT). These models were implemented at the optimal MS levels to identify preliminary information, such as the optimal segmentation level(s) and relevant features, for extracting 12 land use/land cover (LULC) urban classes from the fused datasets. Using the information obtained from the first phase of the analysis, detailed MS classification was iteratively conducted to improve the classification accuracy and derive the final urban LULC maps. Two UAV-based datasets were used to develop and assess the effectiveness of the proposed framework. The hierarchical classification of the pilot study area showed that the RF was superior with an overall accuracy (OA) of 94.40% and a kappa coefficient (K) of 0.938, followed by SVM (OA = 92.50% and K = 0.917) and DT (OA = 91.60% and K = 0.908). The classification results of the second dataset revealed that SVM was superior with an OA of 94.45% and K of 0.938, followed by RF (OA = 92.46% and K = 0.916) and DT (OA = 90.46% and K = 0.893). The proposed framework exhibited an excellent potential for the detailed mapping of heterogeneous urban landscapes from the fusion of UHSR orthophoto and DSM images using various ML models.

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“…For mapping tasks using VHR data, object-based image analysis (OBIA) is preferred over traditional per-pixel classification [6][7][8][9][10], because pixels of a homogeneous land-cover patch often have heterogeneous spectral responses or high information content. Combined with various classification algorithms, OBIA has been routinely used to map detailed urban features with some success [4,11,12]. Previous studies also demonstrated the advantage of data fusion of VHR and LiDAR or synthetic aperture radar (SAR) images for urban-mapping applications [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Assessing Deep Convolutional Neural Networks and Assisted Machine Perception for Urban Mapping

2021

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High-spatial-resolution satellite imagery has been widely applied for detailed urban mapping. Recently, deep convolutional neural networks (DCNNs) have shown promise in certain remote sensing applications, but they are still relatively new techniques for general urban mapping. This study examines the use of two DCNNs (U-Net and VGG16) to provide an automatic schema to support high-resolution mapping of buildings, road/open built-up, and vegetation cover. Using WorldView-2 imagery as input, we first applied an established OBIA method to characterize major urban land cover classes. An OBIA-derived urban map was then divided into a training and testing region to evaluate the DCNNs’ performance. For U-Net mapping, we were particularly interested in how sample size or the number of image tiles affect mapping accuracy. U-Net generated cross-validation accuracies ranging from 40.5 to 95.2% for training sample sizes from 32 to 4096 image tiles (each tile was 256 by 256 pixels). A per-pixel accuracy assessment led to 87.8 percent overall accuracy for the testing region, suggesting U-Net’s good generalization capabilities. For the VGG16 mapping, we proposed an object-based framing paradigm that retains spatial information and assists machine perception through Gaussian blurring. Gaussian blurring was used as a pre-processing step to enhance the contrast between objects of interest and background (contextual) information. Combined with the pre-trained VGG16 and transfer learning, this analytical approach generated a 77.3 percent overall accuracy for per-object assessment. The mapping accuracy could be further improved given more robust segmentation algorithms and better quantity/quality of training samples. Our study shows significant promise for DCNN implementation for urban mapping and our approach can transfer to a number of other remote sensing applications.

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Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

Cited by 19 publications

References 48 publications

Classification of forensic hyperspectral paper data using hybrid spectral similarity algorithms

Classification of forensic hyperspectral paper data using hybrid spectral similarity algorithms

Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification

Assessing Deep Convolutional Neural Networks and Assisted Machine Perception for Urban Mapping

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