Change detection for hyperspectral imagery

Change Detection (CD) is the process of identifying temporal or spectral changes in signals or images. Detection and analysis of change provide valuable information of transformations in a scene. Hyperspectral sensors provide spatial and spectrally rich information that can be exploited for Change Detection. This paper develops and analyzes various CD algorithms for the detection of changes using single-pass and multi-pass Hyperspectral images. For the validation and performance comparisons, changes obtained are compared for the conventional similarity correlation coefficient as well as traditional change detection algorithms, such as image differencing, image ratioing, and principle component analysis (PCA) methods. Another main objective is to incorporate Kernel based optimization by using a nonlinear mapping function. Development of nonlinear versions of linear algorithms allows exploiting nonlinear relationships present in the data. The nonlinear versions, however, become computationally intensive due to the high dimensionality of the feature space resulting in part from application of the nonlinear mapping function. This problem is overcome by implementing these nonlinear algorithms in the high-dimensional feature space in terms of kernels. Kernelization of a similarity correlation coefficient algorithm for Hyperspectral change detection has been studied. Preliminary work on dismount tracking using change detection over successive HSI bands has shown promising results. CD between multipass HSI image cubes elicits the changes over time, whereas changes between spectral bands for the same cube illustrate the spectral changes occurring in different image regions, and results for both cases are given in the paper.