SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data

Gonçalves, João; Pôças, Isabel; Marcos, Bruno; Mücher, C.A.; Honrado, João P.

doi:10.1016/j.jag.2018.11.011

Cited by 43 publications

(45 citation statements)

References 32 publications

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“…GEOBIA concepts and approaches have been applied widely in several domains of image classification and object annotation [46]. Image segmentation is considered the most critical process in GEOBIA to address problems that are associated with conventional pixel-based image processing methods.…”

Section: Methodsmentioning

confidence: 99%

“…Although some segmentation solutions have been reported by researchers [45], the definition of the optimal spatial scale for object definition regarding each specific spatial problem such as gully networks detection is still unclear. Image segmentation in gully and landslide detection studies has been undertaken by visual interpretation or trial-and-error [46].…”

Section: Introductionmentioning

confidence: 99%

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A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Shahabi

Jarihani

Piralilou

et al. 2019

Sensors

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Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Shahabi

Jarihani

Piralilou

et al. 2019

Sensors

View full text Add to dashboard Cite

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“…Therefore, the segmentation effect has an important impact on the subsequent feature extraction, classification, etc. [13]. A large number of segmentation algorithms have been applied in GEOBIA.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images

et al. 2020

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The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the ‘‘Multi-resolution Segmentation’’ algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost.

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“…Accordingly, many types of image classification methods have been proposed. These methods include maximum likelihood classification [1], support vector machine (SVM) [2,3], random forest [4], rotation forest [5], object-oriented image classification methods [6][7][8][9][10], and deep learning-based classification methods [11,12]. However, the results of image classification still cannot achieve 100% accuracy or a sufficiently convincing level of accuracy and sufficient reliability despite significant progress in the field of RS image classification.…”

Section: Introductionmentioning

confidence: 99%

A Modeling and Measurement Approach for the Uncertainty of Features Extracted from Remote Sensing Images

Zhang

Xiao

2019

Remote Sensing

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The reliability of remote sensing (RS) image classification is crucial for applying RS image classification results. However, it has received minimal attention, especially the uncertainty of features extracted from RS images. The uncertainty of image features constantly accumulates, propagates, and ultimately affects the reliability and accuracy of image classification results. Thus, research on the quantitative modeling and measurement of the feature uncertainty of RS images is very necessary. To make up for the lack of research on quantitative modeling and measurement of uncertainty of image features, this study first investigates and summarizes the appearance characteristics of the feature uncertainty of RS images in geospatial and feature space domains based on the source and formation mechanisms of feature uncertainty. Then, a modeling and measurement approach for the uncertainty of image features is proposed on the basis of these characteristics. In this approach, a new Local Adaptive Multi-Feature Weighting Method based on Information Entropy and the Local Distribution Density of Points is proposed to model and measure the feature uncertainty of an image in the geospatial and feature space domains. In addition, a feature uncertainty index is also constructed to comprehensively describe and quantify the feature uncertainty, which can also be used to refine the classification map to improve its accuracy. Finally, we propose two effectiveness verification schemes in two perspectives, namely, statistical analysis and image classification, to verify the validity of the proposed approach. Experimental results on two real RS images confirm the validity of the proposed approach. Our study on the feature uncertainty of images may contribute to the development of uncertainty control methods or reliable classification schemes for RS images.

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SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data

Cited by 43 publications

References 32 publications

A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images

A Modeling and Measurement Approach for the Uncertainty of Features Extracted from Remote Sensing Images

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