A Sensor Specified Method Based on Spectral Transformation for Masking Cloud in Landsat Data

Few uniform approaches designed for masking clouds by means of high resolution sensors have been widely developed due to the divergence in spectral channels. Here we propose an algorithm on a basis of two linear constraints among indexes acquired from the normalized Tasseled Cap (TC) transformation. This sensor specific approach requires neither thermal bands nor other reference images. The diversity of cloud type and cloud coverage (1%-20%) is analyzed for validating the present algorithm using nine scenes of the typical Landsat sensors. The first results derived from Landsat MSS and TM images are assessed in comparison with the Maximum Likelihood supervised classification method. Another comparison is then employed in analyzing the agreement between the automatic cloud cover assessment (ACCA) algorithm and our method. Although varying acceptable degrees of discrepancies are occurred among the above three methods in identifying thin clouds and cloud edges, the overall results show a fine agreement in detecting thick clouds and large clouds. In the event of the acquirement of the sensor-based parameters of TC transformation, this method could be developed for flagging clouds on other high resolution sensors, like SPOT, Quick-Bird, HJ, etc.