Consistent classification of image time series with automatic adaptive signature generalization

Abstract Long-term data archives such as Landsat offer the potential for understanding land cover dynamics over large areas, but limited progress has been made towards realizing this potential due to data availability and computational limitations. Those limitations are less relevant now, and there is renewed interest in developing reliable methods of automatically and consistently classifying time series of remotely sensed images. Our objective was to develop a method of automatically classifying temporally irregular time series (i.e., non-anniversary date images in consecutive years) of images with a minimum of parameterization and a priori information. In contrast to traditional signature extension methods, the automatic adaptive signature generalization procedure (AASG) adapts class spectral signatures to individual images and therefore requires no image correction procedure. Class signatures are derived from pixels with stable land cover through time. We tested the performance of AASG relative to traditional signature extension with various image corrections, and explored the sensitivity of AASG to a thresholding parameter ( c ) controlling stable site identification. AASG performed as well as signature extension with atmospheric correction ( κ  = 0.68), and better than signature extension with relative ( κ  = 0.65) and TOA reflectance ( κ  = 0.56) image corrections for a summer–summer image pair. Additionally, we demonstrated the unique ability of AASG to adapt class signatures to phenological differences by classifying a summer–winter image pair with a modest reduction in overall accuracy ( κ  = 0.66). Observed sensitivity to c supported the hypothesis of an optimum value yielding enough training sites to describe class spectral variability, but conservative enough to minimize contamination of signatures due to classification errors. AASG offers significant advantages over traditional signature extension, particularly for temporally irregular time series. Although we demonstrated a simple implementation, the AASG approach is flexible and we outline several refinements which stand to improve performance. This development represents significant progress towards realizing the potential of long-term data archives to gain long-term understandings of global land cover dynamics.