Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)

Wang, Minye; Fei, Xianyun; Zhang, Yuanzhi; Zhou, C.; Wang, Xiaoxue; Tsou, Jin Yeu; Liu, Dawei; Lu, Xianping

doi:10.3390/rs10050778

Cited by 40 publications

(24 citation statements)

References 29 publications

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“…Huang et al [31] found that pixel-based morphological profiles significantly outperformed object-based GLCM textures for forest mapping and species classification. Wang et al [32] tested the Completed Local Binary Patterns (CLBP) textures originally designed for face recognition and found that the textures were suitable for classifying wetland vegetation using SVM. With recent advances in machine learning, deep learning models (e.g., CNNs) have achieved great success in computer vision and pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data

et al. 2019

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Object-based image analysis (OBIA) has been widely used for land use and land cover (LULC) mapping using optical and synthetic aperture radar (SAR) images because it can utilize spatial information, reduce the effect of salt and pepper, and delineate LULC boundaries. With recent advances in machine learning, convolutional neural networks (CNNs) have become state-of-the-art algorithms. However, CNNs cannot be easily integrated with OBIA because the processing unit of CNNs is a rectangular image, whereas that of OBIA is an irregular image object. To obtain object-based thematic maps, this study developed a new method that integrates object-based post-classification refinement (OBPR) and CNNs for LULC mapping using Sentinel optical and SAR data. After producing the classification map by CNN, each image object was labeled with the most frequent land cover category of its pixels. The proposed method was tested on the optical-SAR Sentinel Guangzhou dataset with 10 m spatial resolution, the optical-SAR Zhuhai-Macau local climate zones (LCZ) dataset with 100 m spatial resolution, and a hyperspectral benchmark the University of Pavia with 1.3 m spatial resolution. It outperformed OBIA support vector machine (SVM) and random forest (RF). SVM and RF could benefit more from the combined use of optical and SAR data compared with CNN, whereas spatial information learned by CNN was very effective for classification. With the ability to extract spatial features and maintain object boundaries, the proposed method considerably improved the classification accuracy of urban ground targets. It achieved overall accuracy (OA) of 95.33% for the Sentinel Guangzhou dataset, OA of 77.64% for the Zhuhai-Macau LCZ dataset, and OA of 95.70% for the University of Pavia dataset with only 10 labeled samples per class.

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Section: Introductionmentioning

confidence: 99%

Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data

et al. 2019

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“…Jia adopted an object-oriented classification method on Landsat data to map the spatial distribution of mangrove areas in coastal wetlands of China and obtained accurate results [44]. Wang explored the potentials of completed local binary patterns (CLBP) in extracting texture feature and applied it to classify vegetation in Yancheng coastal wetlands of China [45]. Unfortunately, determining proper spatial-object features is time consuming and the performance of object-based classifier might diverge greatly across different features selected.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

et al. 2019

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Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands.

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“…One of the areas of application of GLCM textural metrics is land cover classification. Over the years, many studies have used these GLCM textural metrics to either create or improve models that discriminate land cover types [37][38][39][40][41][42]. Treitz et al [43] utilized GLCM textural measures derived from C-band SAR data to improve discrimination of different agricultural crops from a Kappa value of 39% to 78%.…”

Section: Introductionmentioning

confidence: 99%

Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data

2019

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In livestock grazing environments, the knowledge of C3/C4 species composition of a pasture field is invaluable, since such information assists graziers in making decisions around fertilizer application and stocking rates. The general aim of this research was to explore the potential of multi-temporal Sentinel-1 (S1) Synthetic Aperture Radar (SAR) to discriminate between C3, C4, and mixed-C3/C4 compositions. In this study, three Random Forest (RF) classification models were created using features derived from polarimetric SAR (polSAR) and grey-level co-occurrence textural metrics (glcmTEX). The first RF model involved only polSAR features and produced a prediction accuracy of 68% with a Kappa coefficient of 0.49. The second RF model used glcmTEX features and produced prediction accuracies of 76%, 62%, and 75% for C3, C4, and mixed C3/C4 grasses, respectively. The glcmTEX model achieved an overall prediction accuracy of 73% with a Kappa coefficient of 0.57. The polSAR and glcmTEX features were then combined (COMB model) to improve upon their individual classification performances. The COMB model produced prediction accuracies of 89%, 81%, and 84% for C3, C4, and mixed C3/C4 pasture grasses, and an overall prediction accuracy of 86% with a Kappa coefficient of 0.77. The contribution of the various model features could be attributed to the changes in dominant species between sampling sites through time, not only because of climatic variability but also because of preferential grazing.

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Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)

Cited by 40 publications

References 29 publications

Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data

Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data

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