Dynamic image segmentation algorithm in 3D descriptions of remote sensing images

The dynamic image segmentation algorithm with multiple stepwise evaluation machines was applied to resultant the new boundary from image contents. The concept of data fusion is also discussed in this research for making the good decision of image behavior by a 3D image describer. It achieves the high-understanding objects by merging some non-distinct image domains from the training patterns. Image describer contains expert knowledge to extract appropriate behaviors of the identified image patterns through the efficient dynamic image segmentation algorithm. The novel dynamic image segmentation algorithm is directly applied to explore recognitions of remote sensing images, where it can quickly choice the proper partition number of interesting image patterns area and determine their associated central positions. Due to the specific image intensity appropriately represent in the form of 3D description, an approximation object was dynamically generated with the image partition phase and merging stage to find appropriate 3D image describer. This 3D image describer explicitly presents its feature in diverse maps. Finally, the classification problems of three remote sensing images in computer simulations compared with both k-means and Fuzzy c-means (FCMs) methods. The measurement of misclassification error (ME) is selected to present the great results in various remote sensing images segmentation by the designed algorithm.