Low altitude polarization hyperspectral target detection based on CNN method

With the development of unmanned aerial vehicle (UAV) in aeronautical monitoring field, the performance requirements are continuously improved, each application scene also puts forward higher and higher requirements for target detection accuracy and speed. The traditional target imaging method is difficult to meet the image quality requirements, and the artificial target recognition method can't cope with the rapid changes in the detection environment. Combined with the development of deep learning and polarization hyperspectral imaging technology, a ground target detection method based on Faster R-CNN was proposed. We proposed region proposal network (RPN) module for model training. In the target detection phase, the proposed feature map is obtained by pooling operation of interest regions. Finally, we used the proposed feature map to complete the target category classification. Three scale models were used in the experiment, and through polarization hyperspectral camera, the image data of target in different scene conditions was acquired in indoor and outdoor simulation environment for training and validation of models. The experimental results showed that the proposed method could achieve ideal detection accuracy and speed when the ground target was effectively detected.