International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
each iteration excluding the most anomalous samples until the rest samples unchanged. Then the
metric by the rest samples is believed to be immune to the anomalies, or a robust one. However,
the robust metric anomaly detection methods don’t take into consideration the nonlinear
relationship between different bands of the hyperspectral images. The Gaussian assumption of the
target present hypothesis and target absent hypothesis may not be valid either [5]. Besides, on the
boundary between background and anomaly it is very common to find the mixed pixels. So does it
when the size of anomaly is smaller than the images’ spatial resolution. The cases become much
worse when the pixels are seriously mixed, or the nonlinear mixed pixels. Kernel based anomaly
detectors, such as kernel-RX, have been developed to solve the above problem, while the metric in
high dimension feature space is not robust since the anomaly pixels may be contained in the
background gram matrix.
Original feature space Projected into high dimension Find robust anomalous Metric
kernel feature space
Fig.1 The schematic flow sheet of our method. The black dots represent the background
pixels, and the red ones corresponding to the anomaly targets.
This thesis proposes a new anomaly detector by exploiting a robust metric in the kernelized
feature space. The idea is shown in Fig. 1, where vectored pixels in original feature space may not
be fit for Gaussian distribution, but it is the case for some high dimension feature space like the
middle picture with the counter corresponding to the Manhanlobis metric. But only with the
metric excluding the anomaly pixels can the real anomalous degree of the anomalies be presented,
shown in the last picture.
Robust Anomalous Metric in High Dimension Feature Space
Traditional target detection methods exploit the linear separablity between targets and
backgrounds signals [6]. Classic approaches include subspace model and linear mixture model [6].
Due to the lack of prior information on targets, anomaly detection methods depend on the
