Airport Object Segmentation And Recognition Method

The thesis is focused on airport target detection, segmentation and recognition for the remote sensing image. As a widely used technology, object recognition has following characteristics: pertinence and dependence. In the actual application, facing the different object, we always propose different supposes and select different methods. Even for the same object, different methods are selected in allusion to different applications. Based on predecessor work, we hope to make some improvements to their research results.In the aspect of object detection and segmentation, algorithms are classified to four parts by discontinuity and comparability of the pixels. We begin with parallel technology of edge and area segmentation. Then some typical algorithms are introduced. In allusion to the weakness of single algorithm and characteristics of the airport, we propose a method composed of multi-segmentation algorithms. The experiments result show that it works well.In the aspect of recognition, some questions on design recognition system are discussed. We introduce the factors of recognition complication, the expression of airport model, shape-based feature extraction methods. Afterward, based on characteristics of airport, a recognition algorithm using Hough transformation is introduced and the experiments show it works well.In the end, on deeper researching airport objects detection, segmentation and recognition, we discuss the parallel program design. Based on some typical MPP hardware structure, we design parallel airport recognition algorithms.