Combining multi-scale textural features from the panchromatic bands of high spatial resolution images with ANN and MLC classification algorithms to extract urban land uses

Saboori, Mojtaba; Torahi, Ali Asghar; Bakhtyari, Hamid Reza Riyahi

doi:10.1080/01431161.2019.1620371

Cited by 9 publications

(10 citation statements)

References 78 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%

“…However, the solitary optimal window size for the exploitation of texture would not be adequate for images of urban scenes covering land use/cover classes with similar spectral behaviors. Therefore the multiscale texture analysis is appropriate for urban scenes [2,13]. In this study, five different window sizes (e.g., 5 × 5, 9 × 9, 17 × 17, 31 × 31, and 51 × 51); three directions of horizontal (0 • ), diagonal (45 • ), and vertical (90 • ); and four cell shifts of 3, 7, 15, and 30 pixels were tuned to implement the technique.…”

Section: Multiscale Textural Feature Extractionmentioning

confidence: 99%

“…It should be noted that the various directions and cell shifts are only restricted to the second-order textural parameters. For more information about how these cell shifts, directions, and window sizes were selected, see [13].…”

Section: Multiscale Textural 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: Multiscale Textural Feature Extractionmentioning

confidence: 99%

Section: Multiscale Textural Feature Extractionmentioning

confidence: 99%

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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“…Conversely, the selected window size in this study aligns with the actual size of mangrove forests, resulting in higher accuracy. Saboori et al [57] found that a window size of 51 × 51 is most effective for extracting urban land use, and Duan et al [58] discovered that window sizes of 19 × 19 and 23 × 23 improve soil identification accuracy. These findings align with the observations in this paper, where the appropriate window size enhances recognition accuracy by considering the variation in object sizes.…”

Section: Effect Of Texture Window On Classification Resultsmentioning

confidence: 99%

Mangrove Species Classification from Unmanned Aerial Vehicle Hyperspectral Images Using Object-Oriented Methods Based on Feature Combination and Optimization

Ye,

Zhou

2024

Sensors

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Accurate and timely acquisition of the spatial distribution of mangrove species is essential for conserving ecological diversity. Hyperspectral imaging sensors are recognized as effective tools for monitoring mangroves. However, the spatial complexity of mangrove forests and the spectral redundancy of hyperspectral images pose challenges to fine classification. Moreover, finely classifying mangrove species using only spectral information is difficult due to spectral similarities among species. To address these issues, this study proposes an object-oriented multi-feature combination method for fine classification. Specifically, hyperspectral images were segmented using multi-scale segmentation techniques to obtain different species of objects. Then, a variety of features were extracted, including spectral, vegetation indices, fractional order differential, texture, and geometric features, and a genetic algorithm was used for feature selection. Additionally, ten feature combination schemes were designed to compare the effects on mangrove species classification. In terms of classification algorithms, the classification capabilities of four machine learning classifiers were evaluated, including K-nearest neighbor (KNN), support vector machines (SVM), random forests (RF), and artificial neural networks (ANN) methods. The results indicate that SVM based on texture features achieved the highest classification accuracy among single-feature variables, with an overall accuracy of 97.04%. Among feature combination variables, ANN based on raw spectra, first-order differential spectra, texture features, vegetation indices, and geometric features achieved the highest classification accuracy, with an overall accuracy of 98.03%. Texture features and fractional order differentiation are identified as important variables, while vegetation index and geometric features can further improve classification accuracy. Object-based classification, compared to pixel-based classification, can avoid the salt-and-pepper phenomenon and significantly enhance the accuracy and efficiency of mangrove species classification. Overall, the multi-feature combination method and object-based classification strategy proposed in this study provide strong technical support for the fine classification of mangrove species and are expected to play an important role in mangrove restoration and management.

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Quantile random forest technique for soil moisture contents digital mapping, Sarvestan Plain, Iran

Zare,

Abtahi,

Dehghani

et al. 2024

Advanced Tools for Studying Soil Erosion Processes

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Combining multi-scale textural features from the panchromatic bands of high spatial resolution images with ANN and MLC classification algorithms to extract urban land uses

Cited by 9 publications

References 78 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

Mangrove Species Classification from Unmanned Aerial Vehicle Hyperspectral Images Using Object-Oriented Methods Based on Feature Combination and Optimization

Quantile random forest technique for soil moisture contents digital mapping, Sarvestan Plain, Iran

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