Landsat time-series land cover mapping with spectral signature extension method

Time-series remote sensing images were previously employed to detect land use and land-cover changes and to analyze related trends. However,land-cover change mapping using time-series remote sensing data,especially medium-resolution imagery,was often constrained by a lack of high-quality training and validation data,especially for historical satellite images. In this study,we tested and evaluated a generalized classifier for time series Landsat Thematic Mapper( TM) imagery based on spectral signature extension. First,a new atmospheric correction procedure and a robust relative normalization method were performed on time-series images to eliminate the radiometric differences between them and to retrieve the surface reflectance. Second,we selected one surface reflectance image from the time series as a source image based on the availability of reliable ground truth data. The spectral signature was then extracted from the training data and the source image. Third,the spectral signature was extended to all the corrected time-series images to build a generalized classifier. This method was tested on a time series consisting of five Landsat TM images of the Tibetan Plateau,and the results showed that the corrected time-series images could be classified effectively from the reference image using the generalized classifier. The overall accuracy achieved was between 88. 35% and 94. 25%,which is comparable with the results obtained using traditional scene-by-scene supervised classification. Results also showed that the performance of the extension method was affected by the difference in acquisition times of the source image and target image.