Scholarly record
DETECTION OF PEOPLE AND VEHICLES USING VERY HIGH-RESOLUTION SATELLITE IMAGES
Abstract
Processing of satellite data with high and very high spatial resolution enables to monitor selected social activities in open land, such as the occurrence and movement of people, cars, ships and other transport means. Such information is important for mass event control, urban and transport planning, intelligent transport systems, emergency, epidemiologic control, military application etc. The pilot area is located in Prague, close to the Old Town Square. Panchromatic and multispectral imagery from WorldView3 was processed. After radiometric and atmospheric corrections pan-sharpening was applied to improve visual interpretation. Based on object classification approach the following algorithms for segmentation were tested - edge and intensity algorithms, merging using Full Lambda Schedule and Fast Lambda with variable settings. Created list of classes served as a source for selection of representative samples. Suitable spatial, spectral and texture attributes were selected for object-oriented classification using K Nearest Neighbor, Support Vector Machine and Principal Components Analysis. All object classification results were compared and evaluated. Also, convolutional neural networks (YOLOV5) were tested. Segmentation and classification results were validated using results of manual vectorization of target features (vehicles, people) performed by four persons. The detection of vehicles was relatively successful, especially in open places without shade or vegetation. A small size of people’s vertical projection influencing only individual, often mixed, pixels caused difficulties in detection of people. While detection of individual people is not satisfactory yet, classification of people clusters provides promising results, especially in open land such as large streets or squares.
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