Operational Performance of an Automatic Preliminary Spectral Rule-Based Decision-Tree Classifier of Spaceborne Very High Resolution Optical Images

In the last 20 years, the number of spaceborne very high resolution (VHR) optical imaging sensors and the use of satellite VHR optical images have continued to increase both in terms of quantity and quality of data. This has driven the need for automating quantitative analysis of spaceborne VHR optical imagery. Unfortunately, existing remote sensing image understanding systems (RS-IUSs) score poorly in operational contexts. In recent years, to overcome operational drawbacks of existing RS-IUSs, an original two-stage stratified hierarchical RS-IUS architecture has been proposed by Shackelford and Davis. More recently, an operational automatic pixel-based near-real-time four-band IKONOS-like spectral rule-based decision-tree classifier (ISRC) has been downscaled from an original seven-band Landsat-like SRC (LSRC). The following is true for ISRC: (1) It is suitable for mapping spaceborne VHR optical imagery radiometrically calibrated into top-of-atmosphere or surface reflectance values, and (2) it is eligible for use as the pixel-based preliminary classification first stage of a Shackelford and Davis two-stage stratified hierarchical RS-IUS architecture. Given the ISRC “full degree” of automation, which cannot be surpassed, and ISRC computation time, which is near real time, this paper provides a quantitative assessment of ISRC accuracy and robustness to changes in the input data set consisting of 14 multisource spaceborne images of agricultural landscapes selected across the European Union. The collected experimental results show that, first, in a dichotomous vegetation/nonvegetation classification of four synthesized VHR images at regional scale, ISRC, in comparison with LSRC, provides a vegetation detection accuracy ranging from 76% to 97%, rising to about 99% if pixels featuring a low leaf area index are not considered in the comparison. Second, in the generation of a binary vegetation mask from ten panchromatic-sharpened QuickBird-2 and IKONOS-2 images, the operational performance measurement of ISRC is superior to that of an ordinary normalized difference vegetation index thresholding technique. Finally, the second-stage automatic stratified texture-based separation of low-texture annual cropland or herbaceous range land (land cover class AC/HR) from high-texture forest or woodland (land cover class F/W) is performed in the discrete, finite, and symbolic ISRC map domain in place of the ordinary continuous varying, subsymbolic, and multichannel texture feature domain. To conclude, this paper demonstrates that the automatic ISRC is eligible for use in operational VHR satellite-based measurement systems such as those envisaged under the ongoing Global Earth Observation System of Systems (GEOSS) and Global Monitoring for the Environment and Security (GMES) international programs.