Object-oriented feature extraction method for image data compaction

An online unsupervised feature-extraction method for high-dimensional remotely sensed image data compaction is proposed. This method is directed at the reduction of data redundancy in the scene representation of satellite-borne, high-resolution multispectral sensor data. The algorithm partitions the observation space into an exhaustive set of disjoint objects, and pixels belonging to each object are characterized by an object feature. The set of object features, rather than the pixel features, is used for data transmission and classification. Illustrative examples of high-dimensional image data compaction are presented, and the feature representation performance is investigated. Example results show an average compaction coefficient of more than 25 to 1 when this method is used; the classification performance is improved slightly by using object features rather than the original data, and the CPU time required for classification is reduced by a factor of more than 25 as well. The feature extraction CPU time is less than 15% of CPU time for original data classification.<>