Abstract
In this work a new gap-fill technique entitled projection transformation has been developed and used for filling missing parts of remotely sensed imagery. In general techniques for filling missing areas of an image break down into three main categories: first multi-source techniques that take advantages of other data sources (e.g. using cloud free images to fabricate the cloudy areas of other images); the second ones that fabricate the gap areas using non-gapped parts of an image itself, this group of techniques are referred to as single-source gap-fill procedures; and the third group which applies methods that are a combination of both mentioned techniques, therefore they are called hybrid gap- fill procedures. Here a new developed multi-source methodology called “projection transformation for filling a simulated gapped area in Landsat7/ETM+ imagery” is introduced. The auxiliary imagery for filling the gaps is an earlier obtained L7/ETM+ imagery. Quality of the technique was evaluated from three points of view: statistical accuracy measuring, visual comparison, and post classification accuracy assessment. These evaluation indicators are compared to the results obtained from a commonly used technique by the USGS, the Local Linear Histogram Matching (LLHM) [1]. Results show the superiority of our technique over LLHM in almost all aspects of accuracy.
Highlights
Gapping is a typical phenomenon with remote sensing imagery. (As this occurrence could have dynamic and diverse characteristics there are a variety of techniques that could be applied).Construction of gapped areas from satellite imagery is of high interest for visual image interpretation and digital image classification purposes
The objective of this study is to evaluate the capability of a newly developed multispectral projection transformation i.e. Principal Component Transformation (PCT) gap-fill algorithm in comparison to the commonly used Local Linear Histogram Matching (LLHM)
In this paper PCT-based gap-fill approach for filling the gaps in a simulated Landsat/Enhanced Thematic Mapper Plus (ETM+)SLC-off image was presented. This approach uses a fore- and backward principal component transformation over auxiliary imagery to fill the gapped area while the needed statistics for these transformations came from non-gap areas
Summary
Gapping is a typical phenomenon with remote sensing imagery. Construction of gapped areas from satellite imagery is of high interest for visual image interpretation and digital image classification purposes. Imagery gaps can have several reasons, e.g., cloud coverage for optical imagery, shadowed area for SAR data sets, or instrumentation errors e.g. SLC-off problem [1] and line striping [2]; such areas are referred to in this paper as gap areas i.e. the pixels are set to zero (Fig. 2). Gap areas can have different sizes, dimensions, and locations. For instance the striping problem may affect just one column and/or row of pixels while the cloudy area in an image could be more than 50% of a satellite imagery scene [3]. In the literature many methodologies have been proposed for construction of gapped pixels
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