COMBINED APPROACH BASED ON HARALICK AND GABOR FEATURES TO CLASSIFY BUILDINGS PARTIALLY HIDDEN BY VEGETATION

Classification of urban area is important for urban planning, infrastructure management and detection of illegal constructions. However, automatic object recognition in urban environments is difficult due to textural similarity of materials, varying lighting conditions and partial overlap of buildings with vegetation. The identification of buildings partially hidden by green spaces is particularly challenging because their boundaries merge with the surrounding environment, which reduces the accuracy of traditional classification methods. In this study, a stepwise approach to object classification in aerial images is proposed to improve the recognition of buildings partially hidden by vegetation. The analysis was performed in two stages using three-channel high-resolution aerial images acquired from an unmanned aerial vehicle. In the first stage, classification was performed based on Haralick features computed from a co-occurrence matrix of gradations, which allowed the extraction of statistical texture features. However, this was insufficient for accuracy, so in the second stage, a Gabor filter was additionally applied to provide analysis of local texture features, taking into account the frequency and orientation of image elements. The final classification was performed using Random Forest algorithm, which allowed to divide objects into three categories: "buildings", "vegetation" and "buildings partially hidden by vegetation". The classes "buildings" and "vegetation" were considered as auxiliary, providing quality control of the classification and allowing us to focus on improving the recognition of objects partially occluded by vegetation. Experimental results confirmed that the proposed method is effective for recognizing buildings partially hidden by vegetation. The inclusion of the Gabor filter improved the classification accuracy of this class from 0.84 to 0.90, the completeness from 0.74 to 0.86, and the F1-estimation from 0.79 to 0.88. The 11% improvement in completeness is particularly important because it indicates a reduction in the number of missed buildings. In comparison, the classification accuracy of fully visible buildings increased from 0.84 to 0.91 and that of vegetation from 0.88 to 0.95. Thus, the proposed method, which combines global and local texture features, demonstrated high performance to improve the identification accuracy of complex objects whose boundaries merge with the surrounding vegetation.