Japanese knotweed is a foreign invasive plant species that is, due to its increased prevalence and rapid propagation, displacing native plants and inhibiting their growth, thus reducing biodiversity. Some of the key factors in solving this problem, its detection in the early growth and small plant formation phase, the control of its spread dynamics and the development of a spread prediction model. The master's thesis examines the use of object-based image classification for the detection of Japanese knotweed from orthophotos, produced from images taken by the MicaSense RedEdge-M multispectral camera, which was installed on an unmanned aerial vehicle. The selected area by the Mali Graben watercourse, where bigger and smaller plant formations of Japanese knotweed grow, was recorded in three different phenological phases of the plant's development and in two different heights. For the classification of this invasive plant species on high-resolution multispectral images we used the example-based and rule-based object-oriented image analysis, where Japanese knotweed is distinguished from native plants with the use of spectral, texture and spatial attributes.