Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method

Bramhe, Vijendra Singh; Ghosh, S. K.; Garg, Pradeep

doi:10.1080/10106049.2019.1566406

Cited by 15 publications

(4 citation statements)

References 45 publications

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“…First-order statistics are directly derived from the digital number levels, and secondorder ones are calculated based on GLCM record occurrences of pixel pairs in varied directions [4,[11][12][13][14]29]. For the first-order features, we employed mean and variance, and for the second-order features, we used angular second moment, entropy, contrast, correlation, dissimilarity, and homogeneity [4,56,57].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Statistical approaches based on the gray-level co-occurrence matrix (GLCM) were reported as the most beneficial textural analysis [3,4] among four main types of procedures for textual analysis recognized by [5]. This information is helpful for various applications, such as distinguishing crops [6,7] and classifying tree species [8][9][10] and urban mapping [4,[11][12][13][14]. However, using high-dimensional spatial feature sets can result in redundancy among the features; overfitting of the classifiers [15][16][17][18]; building complex models; making model interpretation challenging; and requiring additional computational time, storage, and processing compared to a more optimal input dataset [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Optimum Feature and Classifier Selection for Accurate Urban Land Use/Cover Mapping from Very High Resolution Satellite Imagery

et al. 2022

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Feature selection to reduce redundancies for efficient classification is necessary but usually time consuming and challenging. This paper proposed a comprehensive analysis for optimum feature selection and the most efficient classifier for accurate urban area mapping. To this end, 136 multiscale textural features alongside a panchromatic band were initially extracted from WorldView-2, GeoEye-3, and QuickBird satellite images. The wrapper-based and filter-based feature selection were implemented to optimally select the best ten percent of the primary features from the initial feature set. Then, machine leaning algorithms such as artificial neural network (ANN), support vector machine (SVM), and random forest (RF) classifiers were utilized to evaluate the efficiency of these selected features and select the most efficient classifier. The achieved optimum feature set was validated using two other images of WorldView-3 and Pleiades. The experiments revealed that RF, particle swarm optimization (PSO), and neighborhood component analysis (NCA) resulted in the most efficient classifier and wrapper-based and filter-based methods, respectively. While ANN and SVM’s process time depended on the number of input features, RF was significantly resistant to the criterion. Dissimilarity, contrast, and correlation features played the greatest contributing role in the classification performance among the textural features used in this study. These trials showed that the feature number could be reduced optimally to 14 from 137; these optimally selected features, alongside the RF classifier, can produce an F1-measure of about 0.90 for different images from five very high resolution satellite sensors for various urban geographical landscapes. These results successfully achieve our goal of assisting users by eliminating the task of optimal feature selection and classifier, thereby increasing the efficiency of urban land use/cover classification from very high resolution images. This optimal feature selection can also significantly reduce the high computational load of the feature-engineering phase in the machine and deep learning approaches.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimum Feature and Classifier Selection for Accurate Urban Land Use/Cover Mapping from Very High Resolution Satellite Imagery

et al. 2022

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“…Related studies have shown that the addition of texture features can improve accuracy of ISA mapping [43,44]. Referring to the research of Szantoi et al [45], this study selected the following six second-order texture features with minimal correlation for classification to calculate including Entropy (ENT), Angular Second Moment (ASM), Dissimilarity (DIS), Homogeneity (HOM), Mean (MEAN), and Variance (VAR) [43].…”

Section: Textural Featuresmentioning

confidence: 99%

Large-Scale Impervious Surface Area Mapping and Pattern Evolution of the Yellow River Delta Using Sentinel-1/2 on the GEE

Liu

Zhang

et al. 2022

Remote Sensing

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The ecological environment of Yellow River Delta High-efficiency Ecological Economic Zone (YRDHEEZ) is adjacent to the Bohai Sea. The unique geographical location makes it highly sensitive to anthropogenic disturbances. As an important land surface biophysical parameter, the impervious surface area (ISA) can characterize the level of urbanization and measure the intensity of human activities, and hence, the timely understanding of ISA dynamic changes is of great significance to protect the ecological safety of the YRDHEEZ. Based on the multi-source and multi-modal Sentinel-1/2 remotely sensed data provided by Google Earth Engine (GEE) cloud computing platform, this study developed a novel approach for the extraction of time-series ISA in the YRDHEEZ through a combination of random forest algorithm and numerous representative features extracted from Sentinel-1/2. Subsequently, we revealed the pattern of the ISA spatial-temporal evolution in this region over the past five years. The results demonstrated that the proposed method has good performance with an average overall accuracy of 94.84% and an average kappa coefficient of 0.9393, which verified the feasibility of the proposed method for large-scale ISA mapping with 10m. Spatial-temporal evolution analysis revealed that the ISA of the YRDHEEZ decreased from 5211.39 km2 in 2018 to 5147.02 km2 in 2022 with an average rate of −16.09 km2/year in the last 5 years, suggesting that the ISA of YRDHEEZ has decreased while its overall pattern was not significantly changed over time. The presented workflow can provide a reference for large-scale ISA mapping and its evolution analysis, especially in regions on estuarine deltas.

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“…The gradual development of the remote sensing technology and big data technology offers the possibility of rapidly extracting urban built-up areas (Zhang et al, 2018;Bramhe et al, 2020). In recent years, a large number of high-resolution (12-30 m) builtup area products have been released globally and regionally, such as Fine Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) (Gong et al, 2013) and GlobeLand30 (Chen et al, 2015), which contains built-up areas as of 2010.…”

Section: Introductionmentioning

confidence: 99%

Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas

et al. 2022

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Changes in the spatial expansion of urban built-up areas are of great significance for the analysis of China’s urbanization process and economic development. Nighttime light data can be used to extract urban built-up areas in a large-scale and long-time series. In this article, we introduced the UNet model, a semantic segmentation network, as a base architecture, added spatial attention and channel attention modules to the encoder part to improve the boundary integrity and semantic consistency of the change feature map, and constructed an urban built-up area extraction model—CBAM_UNet. Also, we used this model to extract urban built-up areas from 2012 to 2021 and analyzed the spatial and temporal expansion of China’s urban built-up areas in terms of expansion speed, expansion intensity, expansion direction, and gravity center migration. In the last decade, the distribution pattern of urban built-up areas in China has gradually changed from “center” to “periphery-networked” distribution pattern. It reveals a trend from agglomeration to the dispersion of urban built-up areas in China. It provides a reference for China’s urban process and its economic development.

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Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method

Cited by 15 publications

References 45 publications

Optimum Feature and Classifier Selection for Accurate Urban Land Use/Cover Mapping from Very High Resolution Satellite Imagery

Optimum Feature and Classifier Selection for Accurate Urban Land Use/Cover Mapping from Very High Resolution Satellite Imagery

Large-Scale Impervious Surface Area Mapping and Pattern Evolution of the Yellow River Delta Using Sentinel-1/2 on the GEE

Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas

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