A lossless DEM compression for fast retrieval method using fuzzy clustering and MANFIS neural network

• We focused on the lossless DEM compression for fast retrieval problem.
• A hybrid method between fuzzy clustering and MANFIS neural network was presented.
• The proposed method was validated experimentally on benchmark DEM datasets.
• Its compression ratio is better than those of other relevant algorithms.
• The optimal number of groups of sliding windows was found.

In this paper, we propose an integrated approach between fuzzy C-means (FCM) and multi-active neuro fuzzy inference system (MANFIS) for the lossless DEM compression for fast retrieval (DCR) problem, aiming to compress digital elevation model (DEM) data with the priority of fast retrieval from the client machine over the Internet environment. Previous researches of this problem either used the float wavelet transforms integrated with the SPIHT coding or constructed a predictor model using statistical correlation of DEM data in local neighborhoods; thus giving large-sized compressed data and slow transferring time of data between the server and the client. Based on the observation that different non-linear transforms for predictive values in the sliding windows may increase the compression ratio, we herein present a novel approach for DCR problem and validated it experimentally on the benchmark DEM datasets. The comparative results show that our method produces better compression ratio than the relevant ones.