LAUSR

A novel technique for lossy compression of satellite images aiming at decreasing the impact on vegetation features is presented in this paper. First, the satellite image bands are divided into two groups; the first group contains the most significant bands for vegetation feature extraction (i.e., vegetation group), and the other contains the rest of the spectral bands. After that, a new band ordering algorithm is applied that improves compression performance. The proposed compression technique is based on a new rate control scheme in which the vegetation group is encoded at a higher bit rate than that for the remaining group. We have selected two of the most common indices to assess the impact of lossy compression on the vegetation features in the satellite images; the Normalized Difference Vegetation Index and the Normalized Difference Water Index. The study is performed on several satellite images, where multispectral images are selected from a Landsat ETM$$+$$+ satellite, and hyperspectral images are selected from an Airborne Visible/Infrared Imaging Spectrometer satellite. The experimental results show that the proposed technique is more effective than other traditional compression techniques.