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Evaluation of the Effect of Relative Radiometric Normalization on Multi-date SPOT Image Change Detection
Linear Regression Normalization
Normalized Difference Vegetation Index
Change detection from multi-temporal images has been commonly used in monitoring changes in landscape. However, spurious changes caused by variations of non-surface factors including illumination, viewing geometry, sensor calibration, and atmospheric effects, as well as surface factors including seasonal phonological differences and topographic effects, may lead to inaccurate results, thereby reducing the accuracy of landscape change detection. The study was intended to examine the influence of spectral variations caused by the non-surface factors and phonology on change detection and to confirm the importance of image normalization to change detection. The study also evaluated the performance of three image normalization methods in relation to change detection. Histogram matching (HM), image regression (IR), and pseudo-invariant feature (PIF) regression requiring the use of a reference-subject image pair, were applied to thirteen-date SPOT images from the Wu-Shyr-Keng area of Taichung Prefecture. Three images out of thirteen, acquired in summer, winter, and spring (or fall) were selected as a reference image, respectively. The methods were compared in terms of their capability to improve visual image quality, statistical robustness, and ease of implementation. The HM method was the first in overall performance, IR was the second, and PIF was the last. Image normalization had better performance using visible bands than using NDVI and near-infrared bands, and using red band slightly better than using green band. Low accuracies in change detection were primarily due to the erroneous assignment of no-change pixels to the likely-change class or true-change class. HM and IR improved accuracies effectively, but PIF performed unsteadily. On the other hand, the omission of true-change pixels is the key to change detection in spite of usually occupying a small proportion of the entire image. The three methods performed unsteadily, probably due to insufficient sample of true-change pixels, and further research shound be conducted in the future. The performance also tended to increase with a reduction in season difference between an image pair. An image pair acquired in the same season, particularly in summer, with time difference as short as possible is preferred, but one image of an image pair acquired in winter with the other in summer, or vice versa, should be avoided. Thus image normalization overall is of great importance for change detection.
|Appears in Collections:||森林學系|
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