Calculation of optimal parameters for aircraft recognition on remote sensing imagery by contour analysis

In this paper, we describe the experimental results of aircraft recognition on optical remote sensing imagery using the theory of contour analysis. We propose the new technique to calculate optimal values of the contour’s items quantity and the classification threshold through measuring within- and between-class distances for all possible training set instances combinations with the following detecting and minimizing the type I and II errors. We discuss the construction of contours’ similarity measures combining the principles of finding the most appropriate reference instance and calculating the average value for the whole class. It is shown that the proposed parameters' calculation technique and the similarity function provides training on compact non-uniform datasets and the further of an aircrafts' recognition on images of lower spatial resolution

Agricultural event detection from the satellite image zonal statistics

Satellite images have become an important tool for event detection and monitoring. The key advantage for the satellite based monitoring is their ability to cover large areas frequently which in turn makes them very cost-efficient solution for monitoring geographically large areas. Due to advances in the satellite technology and the image processing techniques, satellites are capable of providing high resolution data within real-time from the Earth’s surface. In this thesis we provide a brief introduction to the satellite based remote sensing and how these methods can be used to model different agricultural events. We inspect theoretical satellite signal responses to a common agricultural events and try to detect these patterns from our own dataset. We develop a method to process satellite images into signals and apply preprocessing methods to increase signal to noise ratio. We then train a gradient boosting classifier to the smoothened signals and process the individual predictions so that we can detect the start and end times for various agricultural events from the agricultural parcels