Interpretation of low altitude aerial images of non-urban environment

In this research project a set of computer vision algorithms for interpretation of the non-urban environment from low altitude aerial images is presented. Considering the size and spread of natural resources in non-urban areas, automating the task of gathering information about various land-covers is of particular importance. The utilization of videos captured by small aerial vehicles has many advantages over traditional high altitude aerial photography or satellite imaging for small scale environmental monitoring and agricultural applications. In this thesis the proposed Modular Interpretation Algorithm (MIA) shifts between the Coarse Tuning Algorithm (CTA), which is computationally efficient and the Fine Tuning Algorithm (FTA), which is capable of finding the target land-cover in complex situations.