Influenced jointly by such factors as ocean, land and atmosphere, the coastal zone is characterized by

the mixing of various land types with high extent of variation. Therefore, traditional spectrum-based classification

cannot meet the demand of precision. The coastal zone is close to ocean water which can be recognized easily by its

significant spectral difference from land objects in the remotely sensed images. Taking advantage of this feature,

this paper proposes a new classification method for coastal zone remotely sensed images by adding space information.

Ocean water is recognized first, and then the distance from every non-ocean-water pixel to its nearest sea water is

calculated. Different objects on the coastal zone have their respective characteristic distances to ocean water. So

helped with the space information, we can improve the precision of classification, especially for the objects with

similar spectral features but different distances to sea water. Using this method, the authors studied the multi-

band QuickBird image of Huangdao island in Qingdao, and the result proves the validity of this method in coastal

areas relative to the pure spectrum method.