Spectrally-consistent relative radiometric normalization for multi-temporal Landsat 8 images

Muhammad Aldila Syariz, Chao Hung Lin, Bo Yi Lin

研究成果: Paper同行評審

摘要

Radiometric normalization is a necessary pre-processing step since the acquired satellite images contain uncertainties such as atmospheric effect and surface reflectance. For most historical experiments, the associated atmospheric properties may be difficult to obtain even for planned acquisitions. Relative normalization is an alternative method whenever absolute reflectance properties are not required. The key to relative normalization is the selection of pseudo-invariant features (PIFs) in an image. PIFs of a bi-temporal image is a group of pixels which are statistically nearly-constant over the period of the bi-temporal image acquisitions. Several methods, such as manual selection, histogram matching, and principal component analysis, had been proposed for PIFs extraction. Yet, a change in pixel’s spectral signature before and after normalization, called spectral inconsistency, is detected whenever those PIFs extraction methods, associated with a regression process, are performed. To overcome this shortcoming, the commonly used PIFs selection, called multivariate alteration detection (MAD), is utilized as it considers the relationship among bands. Further, a constrained regression is adopted to enforce the normalized pixel’s spectral signature to be consistent as possible. This approach is applied to multi-temporal Landsat-8 imageries. Moreover, spectral distance and similarities are utilized for evaluating the consistency of the normalized pixel’s spectral signature.

原文English
出版狀態Published - 2017
事件38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017 - New Delhi, India
持續時間: 2017 十月 232017 十月 27

Other

Other38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017
國家/地區India
城市New Delhi
期間17-10-2317-10-27

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信

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