Cloud-Free Satellite Image Mosaics with Regression Trees and Histogram Matching

This study's objective is to test whether a new strategy foe developing cloud free imagery over a project area can yield image mosaics that permit simple change detection. The strategy proposed first uses regression tree models to predict band values of cloudy pixels in a reference scene from other scene dates. It secondly matches adjacent scenes with histogram matching based only on image overlap areas. Results of the study indicate that the regression tree prediction offers an effective tool for overcoming persistent cloud cover in Landsat imagery. In addition, histogram matching based on image overlap areas permits seamless mosaicing of scenes that have undergone cloud removal with regression tree prediction. The results also show that mosaics resulting from this new strategy can support change detection in persistently cloudy regions.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00991112
• Authors:
  • Helmer, E H
  • Ruefenacht, B
• Publication Date: 2005-9

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 1079-1089
• Serial:
  • Photogrammetric Engineering and Remote Sensing
  • Volume: 71
  • Issue Number: 9
  • Publisher: American Society for Photogrammetry and Remote Sensing
  • ISSN: 0099-1112

Subject/Index Terms

• TRT Terms: Clouds; Histograms; Landsat satellites; Regression analysis
• Uncontrolled Terms: Land cover; Land cover change; Pixel density; Satellite imagery; Satellite meteorology
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01005835
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 3 2005 11:35AM