Object Detection In High Resolution Satellite Imagery Based On Haar Feature

Automatic feature extraction has always been a hot topic in the area ofremote sense image processing, land us classification and land use dynamicmonitoring. There are many well developed methods, such as classification withand without monitoring, object-oriented feature extraction, among which BPNeural Networks and Support Vector Machine methods are better for smallobject detection in the high resolution remote sense image. In general, theabove methods can produce good results, but most of them have the problems oflow efficiency, low classifier-training speed and experience dependent. Basedon the study of vehicle targets detection in WorldView-2full-color image, weproposed a new approach in platform of Microsoft Visual C++for automaticallyidentifying small targets in high-resolution remote sense image. This methoduses OpenCV Library to extract Haar features of samples and then trains theclassifier with Adaboost Algorithm. We discussed the applicability andfeasibility of this method through experimental results and presented thecomplete technical procedure and program scheme. Also we studied the effectof image resolution on the detection of vehicle targets and discussed thepossible scale problem in application.The main content of this article can be summarized as follow:(1) Based on the previous studies, a new method of object identification usingHaar features and Adaboost classification training algorithm is proposed, whichcan be used for small object detection in the high resolution remote sense image.Expand the applications of Adaboost Algorithm.(2) The complete technical procedure and program scheme are summarized forfurther application of special objects detection in high-resolution remote senseimage.(3) The effect of image resolution on the detection of vehicle targets is studied,as well as the necessary resolution for the detected targets of given scale.