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An innovative and practical satellite image product is described that is ideal for applications in Northern Canada because of its wide area coverage and mapping-quality features. This product is generated from a new procedure developed at the Canada Centre for Remote Sensing (CCRS) for processing Landsat 7 imagery, and by extension, imagery from other Earth Observation satellites. By working with multiple satellite passes, each containing the equivalent of multiple scenes, the new procedure could dramatically reduce the turn-around time for generating georeferenced image products, and also increase their geometric and radiometric accuracy compared to those produced by the current methods. The objective of the process has been to generate satellite image mosaics covering large areas (e.g. >500 000 km2) with uniformly distributed errors at sub-pixel resolution. The paper discusses the theoretical basis of a photogrammetric adjustment for satellite imagery and the results obtained from several tests. The process is generic, involving a sensor model, a satellite orbit model and ground control information; thus it may be easily adapted to any satellite that allows for repeat coverage with overlapping paths. By performing an adjustment to correct the satellite position and attitude data prior to the production of orthoimage products, it is possible to create a mosaic with a single resampling process which minimises both the radiometric and geometric resampling artifacts. The results from three separate tests are presented, along with a discussion of the procedures that were followed in each case. All three tests have successfully demonstrated that sub-pixel sample size errors may be consistently obtained over large areas. A by-product process developed to support the measurement of ground control point coordinates for the satellite adjustment was the automatic matching of geographic features such as lakes and islands in vector data format. This has been a significant development in that it has eliminated manual intervention in the measurement of these features in the imagery, allowing the ground control for entire passes containing several scenes to be obtained in minutes instead of hours. [ABSTRACT FROM AUTHOR]

This paper describes single-date and multi-date land-cover classification accuracy results using segment-based, gap-filled Landsat 7 Enhanced Thematic Mapper data compared with Landsat 5 Thematic Mapper data captured one day apart. Maximum likelihood and Decision tree classification algorithms were evaluated. The same training and verification sets of ground data were used for each classification evaluation. For the comparison with the single-date classification, an average decrease of 2.8% in the classification accuracy was obtained with the use of the gap-filled Landsat data. Area estimates for the mid-summer images differed, on average, from 0.6% to 1.9% for a four-class and eight-class classification, respectively. A multi-date land-cover classification was also completed with the addition of a late spring Landsat 5 image, resulting in an average decrease in classification accuracy of 1.8%. [ABSTRACT FROM AUTHOR]