Mixed Raster Content (MRC) coding is an efficient way of coding compound images. The layered model used gives rise to missing data in the foreground and background layers. When using a block-based transform for coding, the usual solution has been to fill in the missing data using some form of interpolation. In this paper we instead present a method using matching pursuit to find the transform coefficents. The presented method gives a gain of up to 1 dB on the tested images, compared to common data filling methods.

State of the art and coming hyperspectral optical sensors generate large amounts of data and automatic analysis is necessary. One example is Automatic Target Recognition (ATR), frequently used in military applications and a coming technique for civilian surveillance applications. When sensors communicate in networks, the capacity of the communication channel defines the limit of data transferred without compression. Automated analysis may have different demands on data quality than a human observer, and thus standard compression methods may not be optimal. This paper presents results from testing how the performance of detection methods are affected by compressing input data with COTS coders. A standard video coder has been used to compress hyperspectral data. A video is a sequence of still images, a hybrid video coder use the correlation in time by doing block based motion compensated prediction between images. In principle only the differences are transmitted. This method of coding can be used on hyperspectral data if we consider one of the three dimensions as the time axis. Spectral anomaly detection is used as detection method on mine data. This method finds every pixel in the image that is abnormal, an anomaly compared to the surroundings. The purpose of anomaly detection is to identify objects (samples, pixels) that differ significantly from the background, without any a priori explicit knowledge about the signature of the sought-after targets. Thus the role of the anomaly detector is to identify “hot spots” on which subsequent analysis can be performed. We have used data from Imspec, a hyperspectral sensor. The hyperspectral image, or the spectral cube, consists of consecutive frames of spatial-spectral images. Each pixel contains a spectrum with 240 measure points. Hyperspectral sensor data was coded with hybrid coding using a variant of MPEG2. Only I- and P- frames was used. Every 10th frame was coded as P frame. 14 hyperspectral images was coded in 3 different directions using x, y, or z direction as time. 4 different quantization steps were used. Coding was done with and without initial quantization of data to 8 bbp. Results are presented from applying spectral anomaly detection on the coded data set.