Image Processing Techniques based Feature Extraction for Insect Damage Areas

Ece Alkan; Abdurrahim Aydın

doi:10.33904/ejfe.1320121

EN

Image Processing Techniques based Feature Extraction for Insect Damage Areas

Abstract

Monitoring of forests is important for the diagnosis of insect damage to vegetation. Detection and monitoring of damaged areas facilitates the control of pests for practitioners. For this purpose, Unmanned Aerial Vehicles (UAVs) have been recently used to detect damaged areas. In order to distinguish damage areas from healthy areas on UAV images, it is necessary to extract the feature parameters of the images. Therefore, feature extraction is an important step in Computer Aided Diagnosis of insect damage monitored with UAV images. By reducing the size of the UAV image data, it is possible to distinguish between damaged and healthy areas from the extracted features. The accuracy of the classification algorithm depends on the segmentation method and the extracted features. The Grey-Level Co-occurrence Matrix (GLCM) characterizes areas texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships. In this paper, texture characteristics of insect damage areas were extracted from UAV images using with GLCM. The 3000*4000 resolution UAV images containing damaged and healthy larch trees were analyzed using Definiens Developer (e-Cognition software) for multiresolution segmentation to detect the damaged areas. In this analysis, scale parameters were applied as 500, shape 0.1, color 0.9 and compactness 0.5. As a result of segmentation, GLCM homogeneity, GLCM contrast and GLCM entropy texture parameters were calculated for each segment. When calculating the texturing parameters, neighborhoods in different angular directions (0,45,90,135) are taken into account. As a result of the calculations made by considering all directions, it was found that GLCM homogeneity values ranged between 0.08 - 0.2, GLCM contrast values ranged between 82.86 - 303.58 and GLCM entropy values ranged between 7.81 - 8.51. On the other hand, GLCM homogeneity for healthy areas varies between 0.05 - 0.08, GLCM contrast between 441.70 - 888.80 and GLCM entropy between 8.93 - 9.40. The study demonstrated that GLCM technique can be a reliable method to detection of insect damage areas from UAV imagery.

Keywords

Image processing , Insect Damage , Gray level co-occurrence matrix

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Details

Primary Language

English

Subjects

Photogrammetry and Remote Sensing

Journal Section

Research Article

Authors

Ece Alkan ^*
0000-0002-1942-313X
Türkiye

Abdurrahim Aydın
0000-0002-6572-3395
Türkiye

Early Pub Date

June 30, 2023

Publication Date

June 30, 2023

Submission Date

June 26, 2023

Acceptance Date

June 30, 2023

Published in Issue

Year 2023 Volume: 9 Number: 1

DOI

https://doi.org/10.33904/ejfe.1320121

IZ

https://izlik.org/JA33SD97EN

APA

Alkan, E., & Aydın, A. (2023). Image Processing Techniques based Feature Extraction for Insect Damage Areas. European Journal of Forest Engineering, 9(1), 34-40. https://doi.org/10.33904/ejfe.1320121

Cited By

Classification of informal settlements using novel object-based machine learning algorithms and drone imagery, Slovo Park, Johannesburg, South Africa

International Journal of Remote Sensing

https://doi.org/10.1080/01431161.2025.2518504

Creative Commons License

The works published in European Journal of Forest Engineering (EJFE) are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Image Processing Techniques based Feature Extraction for Insect Damage Areas

Öz

Image Processing Techniques based Feature Extraction for Insect Damage Areas

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Cited By

Classification of informal settlements using novel object-based machine learning algorithms and drone imagery, Slovo Park, Johannesburg, South Africa

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