Entropy Analysis for Clustering Based Lossless Compression of Remotely Sensed Images

In an Improved K-means clustering algorithm, initial clustering time can be saved by making initial division based on previous clustering results and maintaining the relationship among stable classes. Every pixel in superspace is required to calculate Euclidean distance for clustering, but only calculating and comparing distances with neighbor centers near to the pixel except those far away from it accelerates the clustering process with more and more classes becoming stable. Clustering lossless compression algorithm can efficiently eliminate the interspectral and intra-spectral redundancy at high convergent speed through enhancing intra-class redundancy. The comparison of the parameter analysis of the AVIRIS images with other lossless compression algorithms shows that this clustering lossless compression algorithm is more efficient.