An Improved Compression Algorithm for Hyperspectral Images based on DVAT-SVD

Abstract

Hyperspectral image (HSI) compression has recently become a popular research area in remote sensing applications. However, existing clustering and compression techniques are not based on the intensity value and work by forming static rules. To overcome these issues, a novel lossy (nearly lossless) compression algorithm based on the distributed visual assessment cluster tendency (DVAT) – singular value decomposition (SVD) technique is proposed. At first, the given HSI image is preprocessed by cellular automata filtering to remove the white Gaussian and speckle noise. After that, the preprocessed image is split into bands, which are formed to a distance matrix. Here, the distances between the image bands are predicted to identify pixels with related intensity values. Hence, the DVAT technique is implemented to cluster the nearest distance images. Then, the label of the cluster is optimally selected for segregating the band into a band index. Consequently, the cluster index is formed and the image matrix is clustered based on the label features. After that, the SVD technique is applied to encode the HSI band image. Therefore, the discrete wavelet transform technique is applied for transformation and the transformed cell redundancy check technique is employed to encode the image band. Then, the image is reconstructed by reversing the above processes. The major advantages of the proposed method comprise a cleared clustered output, a correct index value, and the reduction of data losses during compression. During experimental results, the performance of the proposed method was evaluated in terms of the compression ratio, the mean-square error, and the peak signal-to-noise ratio.