Passive IR polarimetric remote sensing of antipersonnel mines using cellular neural networks

Summary form only given. Active IR polarimetric sensing has been successfully applied for the remote sensing of man made objects and, particular, of buried mines. However, the scattering and power/SNR constraints require near overhead viewing. In contrast, passive polarimetric sensing allows detection with much more operationally convenient arrangements which is highly desirable when working in mined lands. In this work, an approach for detecting buried antipersonnel mines based on the dynamic behaviour difference is presented. The basic idea consists of using a sequence of images of the same piece of land at different time intervals which are applied as the input of a reconfigurable cellular neural network (CNN) architecture. Then, a learning algorithm is applied that optimizes both the network parameters and the network topology that best fit the desired behaviour.