Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Directional Morphological Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
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This paper presents a change detection analysis based on a region growing segmentation approach which combines both spectral and spatial information. The test site is a French Alpine protected area, which like many other mountain areas is characterised by a general increase of forest and woody vegetation due to the abandonment of traditional land use practices. Two Landsat images of the years 1984 and 2000 were used and a classification scheme nomenclature based on four vegetation change classes, implying a gradual modification of land cover, was adopted. The accuracy of the change map was assessed both during two visits on the field and using a bi-temporal aerial photographic coverage. A sampling scheme specifically conceived for change detection products was adopted. Error matrices and accuracy indices to assess commission and omission errors of the change maps were generated. The proposed change detection methodology circumvents limitations which are intrinsic to traditional classification procedures based only on spectral information. On the basis of the accuracy assessment, overall accuracy was 90.1% and the increase of woody vegetation turned out to be the vegetation change class better estimated, with user and producer accuracies of, respectively, 62.3% and 70%. However, confusion between the no change and the other vegetation change classes was noticed, due to standard problems encountered in change studies. Advantages and drawbacks of the use of multitemporal aerial photographs as the validation data set are also discussed.