A Fast Method for Objects Detection and Recognition on High Resolution Imagery Based on Geostatistical and Local Cluster Analysis

This paper describes a new methodology to detect and recognize Object on high resolution multi-spectral images, which involves successively: (1) Resampling the image according to the size of the object to reduce the data involved in the computation greatly. (2) Geostatistic method and a local indicator of spatial autocorrelation is used to detect and , more importantly, to locate all the local clusters with high or low reflectance values which are probably the interesting target objects. In this step, by leveraging both spectral and spatial information, the algorithm requires little or no input from user, and hence can be readily automated. (3) Finally, identify objects by extracting the spectral and geometry features in small image block of the original images. The approach is implemented and tested on 1m resolution aerial digital images collected in the east sea of China, and the ships sailing or anchoring on the sea surface are properly extracted.