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.