A spectral-spatial based local summation anomaly detection method for hyperspectral images

• Anomaly detection technique has drawn the increasing interest in hyperspectral imagery processing fields.
• This article proposes a novel local summation anomaly detection method.
• The method combines the multiple local distributions from neighboring local windows with spectral-spatial feature integration.

Anomaly detection is one of the most popular applications in hyperspectral remote sensing image analysis. Anomaly detection technique does not require any prior features or information of targets of interest and has draw the increasing interest in target detection domain for hyperspectral imagery (HSI) in the recent twenty years. From hyperspectral data, the approximately continuous spectral features which are attributed to the high spectral resolution of hyperspectral image can be achieved. Unfortunately, most conventional anomaly detectors merely take advantage of the spectral information in hyperspectral data and rarely give the consideration to spatial information within neighboring pixels. With the development of remote sensing technology, the high spatial resolution can also be acquired by the hyperspectral airborne/spaceborne sensors. Then, further improvement in algorithmic performance may be achieved if both the spectral and spatial information is combined. This article proposes a novel local summation anomaly detection method (LSAD) which combines the multiple local distributions from neighboring local windows surrounding the pixel under test (PUT) with spectral-spatial feature integration. Some other detection performance enhanced operations such as feature extraction and edge expansion are also used. The proposed local summation anomaly detection method makes allowance for exploiting more sufficient local spatial neighboring relationship of local background distribution around the test pixel considered in detection processing. Moreover, summated local background statistics can get better performance in suppressing background materials and extruding anomalies. Feature extraction enables LSAD with robust background feature statistics and edge expansion can ensure no loss of edge detection information. Experiments are implemented on a simulated PHI data and two real hyperspectral images. The experimental results demonstrate that the proposed anomaly detection strategy outperforms the other traditional anomaly detection methods.