and the descriptor defines orientation and feature vectors. The sequence of steps is covered in Figure 2. RANSAC method
Fischler Bolles, 1981 of model fitting with inliers is applied which uses smallest possible data set and enlarges it to
estimate the model parameters. The final result is in the form of registration of aerial and satellite image.
High resolution satellite Image
reference image Aerial Image
sensed image
Calculating descriptor vector
Calculating descriptor vector
Feature Matching using descriptor vectors
Matrix transformation Superimposition of Aerial
Image over Satellite Image Integral image
computation Integral image
computation Blob detection
Blob detection Scale space
representation Scale space
representation
3.3 Target Geolocation
The next step involves tracking a target in the aerial image. The inbuilt tracking module in the UAV tracks the target in
different frames based on the frame rate of the installed camera. Thus, the location of the tracked target is known in a
particular frame of the aerial image in terms of its row and column values. The aerial image is registered to the
georeferenced satellite image which has geolocation of all the points and features in the image. The method involves locating
the position of the tracked target in satellite image with reference to a frame of the aerial image. Hence the location of
the target is determined in the registered image and its coordinates obtained using pre-existing data of the satellite
imagery. The proposed model uses coordinate determination in latitude and longitude which are derived from the registered
image between a satellite image and an aerial image.
3.4 Registration of Subsequent Frames
The registration of the two images locates the centre point of the blobs detected in the two images at a given scale and
computes the descriptor vector for applying the transformation and generating the common image frame. For a real time
tracking module, multiple frames per second of the moving target are captured. Hence, it is necessary to register
subsequent frames to obtain
target’s position and direction of motion in nearly real time. In addition, adverse situation may
arise in operating the aerial vehicle in locations experiencing denial of GNSS signals. In such cases, it is imperative to
obtain the result of registering subsequent images with the initial aerial image using SURF algorithm and thus obtain a
series of overlapping frame grid. The proposed method is faster than the registration of satellite image to each aerial frame
since computation of scale-space and descriptor vector for a particular image is carried out only once. The obtained
information is retained in the system memory and later matched with the computed details of the next frame. The
method is also used to predict the coordinates of the target at a particular frame and at a particular time. The final result of
this module gives a series of aerial images overlapped over the satellite image of the general area.
4. EXPERIMENT AND RESULTS
