Autonomous navigation of a small, slow speed, low altitude unmanned aerial vehicle (UAV) have many potential applications. UAVs are generally used for (i) remote sensing the areas which are difficult to approach, (ii) surveillance, (iii) target designation or jamming, (iv) weapon delivery or as a weapon by itself, etc. Another potential application would be to use them as cost-effective loitering vehicles near the potential enemy sites, creating nuisance value. In most applications, the solution for autonomous navigation is to install inertial navigation systems (INS) on board the flight vehicle and regularly update the INS as often and as accurately as possible. In this paper, different INS updating techniques are briefly mentioned with their advantages and drawbacks, and then a multi-mode image based navigation is proposed. Using several body mounted focal-plane-array imaging sensors, a bigger image is obtained to get sufficient features for matching. The emphasis in this paper is to get vehicle's speed, direction/attitude, and 'running fixes' by using very reliable 'area correlation' tracking. A combination of feature based scene matching along with area correlation is proposed for updating INS. The effort in this paper is to bring out conceptual ideas of image based navigation to make an UAV to perform better and at the same time cost effective.