hazardous waste incinerators, cement plants, as well as many other types of industrial sources. This monitoring is usually performed using extractive sampling systems coupled with infrared spectroscopy techniques. Some recent standoff measurements performed allowed the evaluation of the air quality but not many remote independent methods allow for low uncertainty measurements. Civil engineering Recent research indicates that hyperspectral imaging may be useful to detect the development of cracks in pavements which are hard to detect from images taken with visible spectrum cameras. Biomedical imaging Hyperspectral imaging has also been used to detect cancer, identify nerves and analyze bruises. Data compression In February 2019, an organization founded by the world's major space industries, the Consultative Committee for Space Data Standards (CCSDS), approved a standard for both lossless and near-lossless compression of multispectral and hyperspectral images (CCSDS 123). Based on NASA's fast-lossless algorithm, it requires very low memory and computational resources compared to alternatives, such as JPEG 2000. Commercial implementations of CCSDS 123 include: The European Space Agency's SHyLoC IP core, for lossless compression of up to 1 Gpbs. Metaspectral for both lossless and near-lossless compression, achieving throughputs of over 30 Gbps. Advantages and disadvantages The primary advantage to hyperspectral imaging is that, because an entire spectrum is acquired at each point, the operator needs no prior knowledge of the sample, and postprocessing allows all available information from the dataset to be mined. Hyperspectral imaging can also take advantage of the spatial relationships among the different spectra in a neighbourhood, allowing more elaborate spectral-spatial models for a more accurate segmentation and classification of the image. The primary disadvantages are cost and complexity. Fast computers, sensitive detectors, and large data storage capacities are needed for analyzing hyperspectral data. Significant data storage capacity is necessary since uncompressed hyperspectral cubes are large, multidimensional datasets, potentially exceeding hundreds of megabytes. All of these factors greatly increase the cost of acquiring and processing hyperspectral data. Also, one of the hurdles researchers have had to face is finding ways to program hyperspectral satellites to sort through data on their own and transmit only the most important images, as both transmission and storage of that much data could prove difficult and costly. As a relatively new analytical technique, the full potential of hyperspectral imaging has not yet been realized. See also Acousto-optic tunable filter Airborne real-time cueing hyperspectral enhanced reconnaissance Cathodoluminescence Full spectral imaging HyMap, a widely used hyperspectral