The edge-preservation multi-classifier relearning framework for the classification of high-resolution remotely sensed imagery

Han, Xiao-Jing; Huang, Xin; Li, Jiayi; Li, Yansheng; Yang, Michael Ying; Gong, Jianya

doi:10.1016/j.isprsjprs.2018.02.009

Cited by 20 publications

(20 citation statements)

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“…For the OIC-MCE method, the overall accuracy of three experiment regions after five iterations increased by 8.38%, 7.99%, and 5.58% compared to the accuracy of the initial MCE method, respectively. Therefore, it can be concluded that five iterations were the optimal iterative time for the proposed OIC-MCE method, which is consistent with Han et al's findings [29]. The overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier in this study (Tables 3-5).…”

Section: The Accuracy Of the Oic-mce And Each Single Classifier In Thsupporting

confidence: 91%

“…The total area ratio of CR in three experiment regions after the first five iterations reached 95.24%, 94.31%, and 97.43%, respectively ( Figure 5). This also shows that the increase in iterative time after five iterations was not helpful to improve the overall accuracy [29] because the areal ratio of the remaining ICR was only about 5%. The homogeneous regions were accurately identified in the initial classification and the first few iterations, while the heterogeneous regions and geographical transition zones were gradually identified with the iterative process ( Figure 6).…”

Section: The Self-adaptively Updated Training Samplesmentioning

confidence: 92%

“…The total area ratio of CR in three experiment 312 regions after the first five iterations reached 95.24%, 94.31%, and 97.43%, respectively ( Figure 5). This 313 also shows that the increase in iterative time after five iterations was not helpful to improve the 314 overall accuracy [29] because the areal ratio of the remaining ICR was only about 5%. The…”

Section: The Accuracy Of the Oic-mce And Each Single Classifier In Thmentioning

confidence: 95%

“…Training samples become the most likely information to be introduced during the iterative process. Given the fact that there are limited training samples for land cover mapping in large areas, it is unlikely that new samples would be artificially added during the iterative process [29]. Self-adaptive updating of training samples was an appropriate option.…”

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confidence: 99%

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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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Section: The Accuracy Of the Oic-mce And Each Single Classifier In Thsupporting

confidence: 91%

Section: The Self-adaptively Updated Training Samplesmentioning

confidence: 92%

Section: The Accuracy Of the Oic-mce And Each Single Classifier In Thmentioning

confidence: 95%

mentioning

confidence: 99%

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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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“…Besides, the insufficient spectral bands and uncertain spectral variations may also result in poor results with low classification performance. This is because of the spectral variability among or within land-covers with different types having similar spectra or the same type having dissimilar object spectra [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Superpixel-Guided Filter Feature Extraction and Selection Approach for Classification of Very-High-Resolution Remotely Sensed Imagery

Liu

Tong

et al. 2020

Remote Sensing

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In this article, a novel feature selection-based multi-scale superpixel-based guided filter (FS-MSGF) method for classification of very-high-resolution (VHR) remotely sensed imagery is proposed. Improved from the original guided filter (GF) algorithm used in the classification, the guidance image in the proposed approach is constructed based on the superpixel-level segmentation. By taking into account the object boundaries and the inner-homogeneity, the superpixel-level guidance image leads to the geometrical information of land-cover objects in VHR images being better depicted. High-dimensional multi-scale guided filter (MSGF) features are then generated, where the multi-scale information of those land-cover classes is better modelled. In addition, for improving the computational efficiency without the loss of accuracy, a subset of those MSGF features is then automatically selected by using an unsupervised feature selection method, which contains the most distinctive information in all constructed MSGF features. Quantitative and qualitative classification results obtained on two QuickBird remotely sensed imagery datasets covering the Zurich urban scene are provided and analyzed, which demonstrate that the proposed methods outperform the state-of-the-art reference techniques in terms of higher classification accuracies and higher computational efficiency.

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Introduction to Multimodal Scene Understanding

Yang

Rosenhahn

Murino

2019

Multimodal Scene Understanding

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The edge-preservation multi-classifier relearning framework for the classification of high-resolution remotely sensed imagery

Cited by 20 publications

References 50 publications

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

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

A Multi-Scale Superpixel-Guided Filter Feature Extraction and Selection Approach for Classification of Very-High-Resolution Remotely Sensed Imagery

Introduction to Multimodal Scene Understanding

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