Một số cải tiến kĩ thuật phân cụm cho ảnh viễn thám
AbstractClustering remote sensing images is an emerged topic that interests many researchers. Remote sensing images can have multi bands and high spatial resolution. Among a number of clustering algorithms such as KMeans, C-Means, Watersed, KMeans has been shown to be very performant. However, when applying on large size remote sensing images, converging speed of KMeans algorithm is still slow. Moreover, this algorithm considers only intensity based features and does not take context features of pixels into account. This leads to over/under segmentation. In this paper, we present two improvements on KMeans that we call WIKMeans and CIKMeans. The first improvement is on the initiation of seeds based on Wavelet transform. The second one is we integrate context information into feature vector. Both improvements help to decrease computational time while keeping comparable precision to the original algorithms.
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