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Conference Paper

Destriping of Miscalibrated Aisa Images

Authors
/persons/resource/rogass

Rogaß,  Christian
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/daniel

Spengler,  Daniel
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/mbochow

Bochow,  Mathias
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/segl

Segl,  Karl
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Lausch,  A.
External Organizations;

/persons/resource/charly

Kaufmann,  Hermann
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Citation

Rogaß, C., Spengler, D., Bochow, M., Segl, K., Lausch, A., Kaufmann, H. (2011): Destriping of Miscalibrated Aisa Images, 7th EARSeL SIG Imaging Spectroscopy workshop (Edinburgh, Scotland 2011).


https://gfzpublic.gfz-potsdam.de/pubman/item/item_246303
Abstract
The analysis of hyperspectral images belongs to the main tasks in Remote Sensing. The foregoing linear radiometric correction of registered digital numbers basically assigns the spectral and spatial dependent response of a hyperspectral pushbroom sensor to a physical meaning - radiance. Slopes and offsets of the correction are often determined in laboratory and in-flight calibrations, but may vary over time. This results in striping artefacts which aggravates succeeding processing steps such as atmospheric correction, classification and segmentation. In this work, a new approach is presented, that automatically removes these stripes calculating improved calibration factors without any prior knowledge or user interaction. The algorithm is based on the assessment of spectral and spatial probability distributions and is constrained by specific minimisation principles. Morphological and spatial filtering techniques and additionally a Signal-to-Noise-Ratio related decision tree are implemented to reduce computational effort and to stabilise the solution depending on local spatial entropy. To objectively evaluate the performance of the new approach, the technique was applied to broadly used image processing examples that has been artificially and randomly degraded by sets of multiplicative and additive noise of different distributions as well as miscalibrated AISA DUAL (VNIR and SWIR) scenes. The results clearly show the benefits of the new approach and, concurrently, provide correction facilities for other miscalibrated pushbroom sensor data.