Hyperspectral change detection using temporal principal component anaylsis

This work deals with the detection of changes using hyperspectral images. Change detection is the process of automatically identifying and analyzing regions that have undergone spatial or spectral changes from multi temporal images. Detecting and representing change provides valuable information of the possible transformations a given scene has suffered over time. Change detection in sequences of hyperspectral images is complicated by the fact that change can occur in the temporal and/or spectral domains. This work studies the use of Temporal Principal Component Analysis (TPCA) for change detection in multi/hyperspectral images. Additional methods were implemented in order to compare its results with TPCA. These were: Image Differencing and Conventional Principal Component Analysis. Hyperspectral imagery from different sensors showing different scenarios was used to test and validate the methods presented in this study. The algorithms were implemented using Matlab, and its performances are presented in terms of false alarms and missed changes. Overall results showed that the performance of TPCA was the best, obtaining the smallest error percentage.