Automatic Detection and Correction of Defective Pixels in PRISMA Hyperspectral Data

In satellite hyperspectral sensors, a standard procedure based on homogeneous reference sources is used to regularly update the map of defective pixels (DPs). Unfortunately, this procedure often fails to detect some DPs both because they may arise in the interval between two standard calibration steps and because they may be characterized by subtle or unexpected signal values. The resulting hyperspectral image is affected by residual nonuniformity noise, which correlates in the along-track direction. This noise source reduces the quality of hyperspectral products, such as classification, unmixing, and material detection. In this article, we present a new procedure to find the location of the residual DPs in the detection matrix and we also propose an effective method to estimate the missing radiance values inferring them from the image pixels by leveraging both spatial and spectral correlation. The procedure, here tailored to PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral images, is quite general and can be easily adapted to process images recorded by any satellite pushbroom hyperspectral sensor. The improved image quality yielded by the proposed procedure is first demonstrated qualitatively, by comparing the global RX (GRX) maps on the original image with those obtained after detection of the DPs and correction of their radiance values. The image quality improvement is then quantified on a set of nine PRISMA images, recorded in an interval of about two years, using two ad hoc defined indexes. The analysis is carried out separately for the visible and near-infrared (VNIR) and shortwave infrared (SWIR) spectrometers of the PRISMA mission.