Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery

Akçay, Özgün; Avşar, Emin Özgür; Inalpulat, Melis; Genç, Levent; Cam, Ahmet

doi:10.3390/ijgi7110424

Cited by 16 publications

(10 citation statements)

References 60 publications

(74 reference statements)

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“…The accuracy of the model is improved by the overall decision as complemented by weak decision trees. The RF method has been widely used in image classification (Akcay et al 2018;G. Cai et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Luo

Yan

McClure

et al. 2022

Environ Sci Pollut Res

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Correlations between socioeconomic factors and poverty in regression models do not reflect actual relationships, especially when data exhibit patterns of spatial heterogeneity. Spatial regression models can estimate the relationships between socioeconomic factors and poverty in defined geographical areas, explaining the imbalanced distribution of poverty, but the relationships between these factors and poverty are not always linear however, and conventional simple linear local regression models do not accurately capture these nonlinear relationships. To fill this gap, we used a local regression method, geographically weighted random forest regression (GW-RFR), that integrates a spatial weight matrix (SWM) and random forest (RF). The GW-RFR evaluates the spatial variations in the nonlinear relationships between variables. A county-level poverty data set of China was employed to estimate the performance of the GW-RFR against the random forest (RF). In this poverty application, the value of was 0.128 higher than that of the RF, the NRMSE value was 1.6% lower than the RF, and the MAE value was 0.295 lower than the RF. These results showed that the relationship between poverty factors and poverty varies with space at the county level in China, and the GW-RFR was suitable for dealing with nonlinear relationships in local regression analysis. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-17513-3.

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Section: Introductionmentioning

confidence: 99%

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Luo

Yan

McClure

et al. 2022

Environ Sci Pollut Res

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“…All selected OLI images were incorporated in the segmentation process, and all bands of selected OLI images weighted equally. "Scale", "Shape", and "Compactness" parameter are three core parameters for this algorithm, which would determine directly the final segmentation result [47]. By referring to the similar research of author's team [48], the parameters in this study were set as 25, 0.1, and 0.7, respectively.…”

Section: Object-oriented Multi-resolution Segmentation and Classificamentioning

confidence: 99%

OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification

Lei

Bian

et al. 2020

Remote Sensing

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Land cover samples are usually the foundation for supervised classification. Unfortunately, for land cover mapping in large areas, only limited samples can be used due to the time-consuming and labor-intensive sample collection. A novel and practical Object-oriented Iterative Classification method based on Multiple Classifiers Ensemble (OIC-MCE) was proposed in this paper. It systematically integrated object-oriented segmentation, Multiple Classifier Ensemble (MCE), and Iterative Classification (IC). In this method, the initial training samples were updated self-adaptively during the iterative processes. Based on these updated training samples, the inconsistent regions (ICR) in the classification results of the MCE method were reclassified to reduce their uncertainty. Three typical case studies in the China-Pakistan Economic Corridor (CPEC) indicate that the overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier. After five iterations, the overall accuracy of the OIC-MCE approach increased by 5.58%-8.38% compared to the accuracy of the traditional MCE method. The spatial distribution of newly added training samples generated by the OIC-MCE approach was relatively uniform. It was confirmed by ten repeated experiments that the OIC-MCE approach has good stability. More importantly, even if the initial sample size reduced by 65%, the quality of the final classification result based on the proposed OIC-MCE approach would not be greatly affected. Therefore, the proposed OIC-MCE approach provides a new solution for land cover mapping with limited samples. Certainly, it is also well suited for land cover mapping with abundant samples.Remote Sens. 2020, 12, 987 2 of 21 various homogeneous or heterogeneous data sources, advanced machine learning algorithms, and a variety of sampling and classification strategies have been introduced into land cover mapping researches [1,[5][6][7].Supervised classification is the most commonly used method in land cover mapping studies, especially for generating a regional or even a global land cover product [8]. Samples are the foundation of this method. The quantity and quality of samples would have a significant impact on the accuracy of land cover products [9,10]. There are many methods to collect samples, such as field survey, crowdsourcing, and manual interpretation [11]. In general, field survey has been a simple and practical method, but it cannot be conducted in inaccessible regions [12]. More importantly, it is time-consuming and labor-intensive. Manual interpretation of high-resolution remote sensing images, relying on the interpretation experiences of the interpreters, has been an effective way to fill the blank of field surveys. However, the subjectivity of the interpreter might affect the objectivity of samples [10]. Crowdsourcing was a new technology that helps to collect samples [13,14]. Unfortunately, it is still in the experimental stage at present, with few available samples and a lack of objective evaluation for their reliability...

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“…As a result of successful image segmentation, the number of elements as a basis for the following image classification is enormously reduced. The quality of classification is directly affected by segmentation quality [17,18]. In the last decade, deep learning methods have proven great success especially in the computer vision area, and became a standard tool for many applications like object detection, artificial intelligence, scene recognition for automation, etc.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+

Akçay

Kinaci

Avşar

et al. 2021

IJGI

Self Cite

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In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that considers geospatial imagery was developed based on the DeepLabV3+ architecture. As a result, the spectral datasets other than RGB provided increments in semantic segmentation accuracies when they were used as additional channels to height information. Furthermore, both the given data augmentation and Tversky loss function which is sensitive to imbalanced data accomplished better overall accuracies. Also, it has been shown that the new dual-stream architecture using Potsdam and Vaihingen datasets produced 88.87% and 87.39% overall semantic segmentation accuracies, respectively. Eventually, it was seen that enhancement of the traditional significant semantic segmentation networks has a great potential to provide higher model performances, whereas the contribution of geospatial data as the second stream to RGB to segmentation was explicitly shown.

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Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery

Cited by 16 publications

References 60 publications

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

Socioeconomic and environmental factors of poverty in China using geographically weighted random forest regression model

OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification

Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+

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