Multi-Source Remotely Sensed Data Combination: Projection Transformation Gap-Fill Procedure

Area of Science:

• Remote Sensing
• Geospatial Analysis
• Image Processing

Background:

• Filling missing data in remotely sensed imagery is crucial for accurate analysis.
• Existing gap-fill techniques are categorized into multi-source, single-source, and hybrid approaches.
• Landsat 7/ETM+ imagery often contains gaps requiring effective reconstruction methods.

Purpose of the Study:

• To introduce and evaluate a novel multi-source gap-fill technique called projection transformation.
• To compare the performance of projection transformation against the established Local Linear Histogram Matching (LLHM) method.
• To assess the effectiveness of projection transformation for simulated gapped areas in Landsat 7/ETM+ imagery.

Main Methods:

• Developed a new multi-source gap-fill technique: projection transformation.
• Utilized earlier Landsat 7/ETM+ imagery as auxiliary data for gap filling.
• Evaluated the technique through statistical accuracy, visual comparison, and post-classification assessment.

Main Results:

• Projection transformation demonstrated superior performance compared to LLHM.
• The new technique achieved higher accuracy across statistical, visual, and classification metrics.
• Effectiveness was confirmed for filling simulated gaps in Landsat 7/ETM+ data.

Conclusions:

• Projection transformation is a highly effective multi-source technique for gap filling in remotely sensed imagery.
• The developed method offers significant advantages over existing techniques like LLHM for Landsat data.
• This advancement provides a more reliable solution for reconstructing missing information in satellite imagery.