ABSTRACT: 
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 lm resolution aerial digital images collected in the east sea of China, and the ships sailing or anchoring on the sea 
surface are properly extracted. 
1. INTRODUCTION 
Object detection has always been one of the most popular 
researches in remote sensing science. From an application point 
of view, the object detection is usually defined as carrying on 
localization and recognition process on remote sensing data for 
interesting target. Object detection can be divided into two 
different stages, namely target searching and localization 
(dynamic question) and recognition and confirmation (static 
question) . In the static question, targets are located, needing 
further recognition and confirmation about attributes of the 
targets. In this stage, efficiency is not very important because of 
the accuracy request. However, in the dynamic question, target 
position is unknown; therefore, searching time is one of the 
most important evaluation criterions. 
In recent years, high-resolution satellite and aerial imagery has 
recently become a new data source for extraction of small-scale 
objects such as vehicles, roads, ship and so on...This paper 
concern locating and extracting ships from the high resolution 
aerial image. At present, most of the ship detection studies are 
based on the Synthetic Aperture Radar (SAR) images and only 
a small number of the ship extraction researches uses 
panchromatic band images of high-resolution satellites. 
Therefore, the usual ship extraction algorithms are actually 
detection of light-target on dark-background. There are three 
notable aspects of limitation. Firstly, band limitation will cause 
a waste of spectral information. Secondly, current algorithms 
only concern a small block of data, which indeed includes the 
target object, but ignore developing method of searching 
potential targets on large imagery. Thirdly, a common and also 
very important difficulty in remote location and target 
recognition is the low efficiency and long calculation time 
when massive data is used. Since existing algorithms can hardly 
meet the fast increasing demand for real-time information 
management, we try to development a novel algorithm which is 
a kind of multi-scale strategy of information extraction based 
on geostatical and local cluster analysis. 
An increase of use of spatial statistics in the analysis of 
remotely sensed data has occurred in the last decade. In 
particular; geostatistics offers a broad range of techniques that 
allow not only the characterization of multivariate spatial 
correlation, but also the spatial decomposition or filtering of 
signal values [Goovaerts, 2002]. The approach known as 
factorial kriging relies on semivariogram to detect multiple 
scales of spatial variability, followed by the decomposition of 
spectral values into the corresponding spatial components. This 
technique was first used in geochemical exploration to 
distinguish large isolated values from group wise anomalies that 
consisted of two or more neighbouring values just above the 
chemical detection limit. 
The LISA (local indicator of spatial autocorrelation) statistic 
allows the comparison of an observation (i.e., a single pixel or 
small group of pixels) with the surrounding ones, followed by a 
test procedure to assess whether this difference is significant or 
not. This approach has been used recently to detect spatial 
outliers in soil samples [McGrath, Zhang, 2003], while the 
LISA has been introduced to quantify the degree of spatial 
homogeneity in remotely sensed imagery. The novelty of the 
proposed approach lies in the geostatistical filtering of the 
image regional background prior to testing the significance of 
LISA values through randomization, and the development of 
two new statistics to combine test results across multiple 
spectral bands. 
2. SHIP DETECTION BASED ON GEOSTATISTICAL 
AND LOCAL CLUSTER ANALYSIS 
The method put forward in this paper is an automatic target 
detection process, which capitalizes on both spatial and spectral 
bands correlation and does not require any a priori information 
on the target spectral signature. The technique does not allow 
discrimination between types of anomalies.