Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China

Yang, Xiangdong; Chen, Longgao; Li, Yingkui; Chen, Longqian

doi:10.1007/s10661-015-4667-3

Cited by 19 publications

(8 citation statements)

References 39 publications

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“…The study pointed out that the synergy between a robust classifier, such as SVM, and the integration of a geometric rule-set and the proposed density indices (UDI and GDI), is a reliable method to improve the urban land cover classification in complex urban environments. The findings in the present study concur with previous studies e.g., [25,[73][74][75], where the rule-based approach using geometric features, texture measurements and the spectral band threshold were found useful for land cover classification enhancement. Some of the features to ingest in the feature space include the bands' mean and standard deviation, and in particular the geometric features related to the object's extent and shape, such as compactness, asymmetry and rectangular fit, area, width and length.…”

Section: Discussionsupporting

confidence: 93%

WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

2019

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The emergence of high-resolution satellite data, such as WorldView-2, has opened the opportunity for urban land cover mapping at fine resolution. However, it is not straightforward to map detailed urban land cover and to detect urban deprived areas, such as informal settlements, in complex urban environments based merely on high-resolution spectral features. Thus, approaches integrating hierarchical segmentation and rule-based classification strategies can play a crucial role in producing high quality urban land cover maps. This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized by a subtropical highland climate. A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral indices, gray level co-occurrence matrix (GLCM) texture measures and a digital terrain model (DTM). In the initial classification, confusion existed among the informal settlements, the high- and low-density built-up areas, as well as between the upland and lowland agriculture. To improve the classification accuracy, a framework based on a geometric ruleset and two newly defined indices (urban density and greenness density indices) were developed. The novel framework resulted in an overall classification accuracy at 85.36% with a kappa coefficient at 0.82. The confusion between high- and low-density built-up areas significantly decreased, while informal settlements were successfully extracted with the producer and user’s accuracies at 77% and 90% respectively. It was revealed that the integration of an object-based SVM classification of WorldView-2 feature sets and DTM with the geometric ruleset and urban density and greenness indices resulted in better class separability, thus higher classification accuracies in complex urban environments.

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Section: Discussionsupporting

confidence: 93%

WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

2019

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“…Users can access all rules and their corresponding information, such as the rule ID, components of a rule (segment class for every selected band), true class label, probability (relative frequency) of every potential class label, rule entropy and hit ratio (accuracy) (Figure 2), while they are always connected to their corresponding pixels. This beneficial trait is highly valued in geoscience and remote sensing applications, especially in the context of land-use and land-cover mapping applications [38,78,79]. To be able to assign every pixel to a map class, each pixel should have at least one matching rule from various rule sets.…”

Section: The Overall Accuracy Of Classificationmentioning

confidence: 99%

A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

et al. 2019

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This paper assesses the performance of DoTRules-a dictionary of trusted rules-as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training sample sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products.Bayesian [17], maximum margin [18], evolutionary [19], clustering [9], association rule learning [20], artificial neural network [12,21,22] and deep learning [23] methods (see Figure 1). Regardless of the classification performance, many of these algorithms act as black-boxes, resulting in a poor recognition of the classification structure and robustness owing to the high-dimensionality of the data [24,25].

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“…Pesticides, also known as agrochemicals or agricultural growth regulators, have played a vital role in meeting the attendant requirements for agricultural production [3,4]. However, due to the poor dispersion, sedimentation, and low biological activity of currently available pesticides at typical dosages, much of these materials are not only wasted, but also pose a threat to local ecosystems; the misuse of pesticides leads to more than 70.6% rivers being polluted in China [5][6][7][8][9]. Silent Spring, published in 1962, was one of the first warnings of the potential for pesticides to damage the environment [10].…”

Section: Introductionmentioning

confidence: 99%

A Facile Approach to Increasing the Foliage Retention of Pesticides Based on Coating with a Tannic Acid/Fe3+ Complex

Zhi

Yao

et al. 2020

Coatings

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The effective utilization of many conventional pesticide formulations is less than 30%, which can increase the environmental impact of these substances. This degree of waste could be reduced by improving the adhesion of pesticides to foliage. In the present work, a complex comprising tannic acid (TA) and Fe3+ ions was used to encapsulate azoxystrobin and avermectin water dispersible granule (WDG) formulations (termed Az-WDG-TA and Av-WDG-TA) to improve adhesion. The treated pesticides exhibited improved photostability as well as sustained continuous release behavior. The retention proportions of the Az-WDG-TA and Av-WDG-TA on cucumber and lettuce foliage were improved by more than 50%. The ability of solutions of these materials to wet foliage was also enhanced after coating, such that the toxicity of Av-WDG-TA to aphids and the antifungal activity of Az-WDG-TA to Fusarium oxysporum were increased by nearly 50%. Given the low cost of TA and Fe3+ compounds and the simple synthesis process, this method represents a promising means of producing foliage-adhesive pesticide formulations with increased retention and bioavailability.

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Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China

Cited by 19 publications

References 39 publications

WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

A Facile Approach to Increasing the Foliage Retention of Pesticides Based on Coating with a Tannic Acid/Fe3+ Complex

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