Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms

Iqbal, Naveed; Mumtaz, Rafia; Shafi, Uferah; Zaidi, Syed Sajjad Haider

doi:10.7717/peerj-cs.536

Cited by 75 publications

(32 citation statements)

References 24 publications

(27 reference statements)

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“…The tassel cap transformation coefficients for Landsat 8 OLI were proposed by Baig in 2014 [54]. The textural features were obtained using the grayscale co-occurrence matrix (GLCM) calculations [55][56][57].…”

Section: Desertification Classification Indicatorsmentioning

confidence: 99%

Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Chen

Shen

et al. 2022

Sustainability

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The ecological stability of mining areas in Northwest China has been threatened by desertification for a long time. Remote sensing information combined with machine learning algorithms can effectively monitor and evaluate desertification. However, due to the fact that the geological environment of a mining area is easily affected by factors such as resource exploitation, it is challenging to accurately grasp the development process of desertification in a mining area. In order to better play the role of remote sensing technology and machine learning algorithms in the monitoring of desertification in mining areas, based on Landsat images, we used a variety of machine learning algorithms and feature combinations to monitor desertification in Ningdong coal base. The performance of each monitoring model was evaluated by various performance indexes. Then, the optimal monitoring model was selected to extract the long-time desertification information of the base, and the spatial-temporal characteristics of desertification were discussed in many aspects. Finally, the factors driving desertification change were quantitatively studied. The results showed that random forest with the best feature combination had better recognition performance than other monitoring models. Its accuracy was 87.2%, kappa was 0.825, Macro-F1 was 0.851, and AUC was 0.961. In 2003–2017, desertification land in Ningdong increased first and then slowly improved. In 2021, the desertification situation deteriorated. The driving force analysis showed that human economic activities such as coal mining have become the dominant factor in controlling the change of desert in Ningdong coal base, and the change of rainfall plays an auxiliary role. The study comprehensively analyzed the spatial-temporal characteristics and driving factors of desertification in Ningdong coal base. It can provide a scientific basis for combating desertification and for the construction of green mines.

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Section: Desertification Classification Indicatorsmentioning

confidence: 99%

Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Chen

Shen

et al. 2022

Sustainability

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“…These features, such as contrast (con), correlation (corr), entropy (ent), and angular second moment (asm), are also widely used as texture features in LCC. (23)(24)(25) In the process of spectral feature extraction, we found that some bands were sensitive to specific landcover objects. To examine whether the texture information of these bands is also effective for these landcover objects, we applied bands 8, 4, 3, and 2 of the Sentinel-2 satellite MSI to calculate the above four texture features based on the GLCM.…”

Section: Swir Red Nir Blue Si Swir Red Nir Bluementioning

confidence: 99%

Feature Selection Method for Open-pit Mine Land Cover Classification Based on Multi-feature Set Using Sentinel-2

Wang¹,

Liu²,

Liu³

2022

Sensors and Materials

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The land cover map is the basis of monitoring changes in open-pit mines. However, owing to the limitation of sensor spectral resolution, the misclassification of pixels is inevitable. To reduce the influence of misclassification on the accuracy of open-pit mine land cover classification (LCC), a feature selection method for open-pit mine LCC based on a multi-feature set using Sentinel-2 images is proposed in this study. First, Sentinel-2 images and shuttle radar topographic mission (SRTM) digital elevation models (DEM) are employed to extract multi-features, including spectral features, topographic features, texture features, and filter features. Then permutation importance (PIMP) feature selection is proposed for selecting optimal features from the multi-feature set. Finally, the results of a practical experiment in Xuzhou City of China are used to verify the validity of the proposed feature selection method. The experimental results show that the multi-feature set can improve the accuracy of open-pit mine LCC and that elevation is the most important feature variable in open-pit mine LCC. Moreover, the PIMP feature selection method can effectively optimize feature combinations to obtain the optimal feature subset. This study provides a useful reference for multi-feature extraction and optimal feature selection in open-pit mine LCC using Sentinel-2 image data.

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“…GLCM has been extensively explored in various application fields for the extraction of textural details of an image. Iqbal et al [52] explores the potential of GLCM for classification of crops from remote sensed images and used different textural feature extractors including GLCM with neural networks for classification problems. The work presented in [54] performs classification of benign and malignant tumors using GLCM.Similarly, the works [55,56] illustrate the potential of GLCM in other application areas such as autonomous cleaning robots.…”

Section: Formulation Of Proposed Contour Energymentioning

confidence: 99%

Fused Second-Order MR Image Feature Framework for Region of Interest Delineation

Banday¹,

Khan²,

Mir³

2021

Preprint

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Healthcare infrastructure relies on technology-driven solutions such as CAD systems for improving the overall efficiency of its procedures and processes. Image segmentation is one of the most critical phases for such systems in view of the fact that accuracy of this phase determines the efficacy of the later phases, to a large extent. Extensive research is underway to develop segmentation techniques that can achieve highest accuracy with some suggestions directed towards an information fusion based approach within the machine learning paradigm. This research paper proposes a fused second-order statistical image feature framework for Region of Interest delineation. It is a feature fusion-based segmentation approach (ACM-FT) that fuses texture driven feature maps from GLCM , GLRLM and Gabor filters. The proposed approach is then compared with Active Contour Model with classical edge detection method (ACM-ED) and Active Contour Model without edges (ACM-WE) using Overlap Index (OI) and Jackard’s Similarity Co-efficient (JSI). The proposed approach achieves an average accuracy of 92.17% and 93.19% for JSI and OI, respectively, demonstrating significant improvements.

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Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms

Cited by 75 publications

References 24 publications

Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Feature Selection Method for Open-pit Mine Land Cover Classification Based on Multi-feature Set Using Sentinel-2

Fused Second-Order MR Image Feature Framework for Region of Interest Delineation

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