Preference aggregation method in determining brightness threshold values for object recognition on optical images

Abstract

Relevance. The need to automate the processing large volumes of geophysical information extracted from images obtained through aerial photography or space remote sensing. The main stage of image processing for object recognition is segmentation. It consists in dividing the image into two homogeneous, clearly distinguishable non-overlapping areas with known boundaries. Threshold segmentation methods are simple but effective and therefore popular. Aim. To describe the experimental studies results of application of the robust method of interval fusion with preference aggregation previously developed by the authors for selecting threshold values when segmenting objects in optical images. Objects. Coastline images taken by Earth remote sensing satellites. Methods. The analyzed image is divided into equal bands. Based on the brightness histogram of each band, its characteristic brightness interval is determined. For the obtained brightness intervals, the fusion result is calculated using the interval fusion with preference aggregation method. The latter is used as a threshold brightness value when forming a segmented image. Results. The results of experimental studies of 100 satellite images of the coastline showed that the proposed method provided correct separation of land and sea regions in 84 images. And the traditional methods of maximum entropy, arithmetic averaging and Otsu showed correct results only in 27, 76 and 73 cases, respectively. Evaluations of such the metrics as Precision, Recall and deviation of the calculated threshold from a known correct value showed the dominance of the proposed method over other tested ones in terms of segmentation quality.