Objective

2

Anomalies typically refer to pixels that evidently reflect different spectral features in a homogeneous background. The regions of the background refer to the pixels that occupy a larger proportion in an image than anomalies. The anomaly detection of hyperspectral remote sensing aims to find the pixels that exhibit distinct spectral features compared with its surrounding pixels. Traditional anomaly detection methods mainly distinguish anomalies by describing the spectral characteristic with a background statistic model

and numerous anomaly detection algorithms are proposed to obtain the uncontaminated statistic model for hyperspectral imagery (HSI). However

the different proportions of ground objects and complex spectrum features of hyperspectral imagery constantly induce a poor representation of the background statistic model. The difference between anomaly and non-anomaly will be weakened

and the final discrimination results will be inaccurate given the existing anomalies in the background statistic model. The spectral character of small background clusters will be covered by large clusters because of the different scales of ground objects and difference in spectral character. The background statistic model cannot be described accurately by extracting all the background pixels simultaneously considering the diversity of the background objects. This paper proposes a novel algorithm for extracting the background model based on the maximum relative density analysis to obtain an accurate background statistic model. An iterative clustering method is applied to address the abovementioned problem and avoid contamination in the background statistic model.

Method

2

Iterative screening is applied to extract background objects gradually to obtain small-scale background clusters without interference from the complex spatial distribution and texture feature of ground objects. The relative similarity density for each pixel is defined by the total number of pixels that have similar spectral features under the concept of relative density analysis. Pixels with the largest relative density will be selected as the cluster center under the current scale

driven by the similarity distribution density. One of the background objects will be extracted adaptively on the basis of the cluster center. In each iteration

we only add the cluster with the largest population to the background statistic model. The background pixels will be extracted when the iteration process is terminated

and the background can be modeled by the spectral features of the obtained clusters. The details of the proposed algorithm are as follows. First

the Euclidean distance between pixels is selected as similarity features. Second

the density and central degree of a pixel can be obtained on the basis of the distance from other pixels in the dataset. We select the pixels with high density and central degree as the center of clusters

and then classify the entire hyperspectral data. Finally

classical Mahalanobis distance is used to measure the anomaly salience of the detection results. The pixels with large Mahalanobis distances according to the value of anomaly salience are likely to be anomalies. Simulation experiments were conducted with two sets of hyperspectral imagery

namely

HyMap

and HYDICE; these sets of hyperspectral imagery are commonly used for experiment. The algorithm was compared with classical algorithms

such as CBAD

LRX

and 2DCAD

in the experiments. The detection results were analyzed using ROC and AUC as the evaluation criteria.

Result

2

The experimental data indicate that the ROC is generally better in the proposed algorithms than in other algorithms; the AUC tested in two hyperspectral data is higher by at least 5.6% and 13.6% than similar algorithms. The comparison of the computational costs between the proposed and the other algorithms is also presented. The computational cost of the proposed algorithm is not superior

but the comprehensive performance of our method is generally optimal. The proposed and the previous methods differ in two points. The proposed method classifies the pixels according to the degree of difference by the data characteristics

and the result obtained by the proposed method is unaffected by various complex scenes. Furthermore

the proposed method considers the scale of the ground object. Thus

the iterative strategy is applied to focus on the pixels with strongest background feathers in the current iteration and avoid the interference caused by existing different scale backgrounds. The proposed algorithm is stable under two complex scenes. Results show the robustness and effectiveness of the proposed method.

Conclusion

2

The pixels with a high degree of anomaly salience are obtained according to the characteristics of the data without constructing the classification surface and setting the number of categories prior. Experimental results also verify that the proposed method improves the effectiveness of the background statistic model and obtains an accurate detection result. The algorithms that can characterize the clutter background accurately will achieve favorable results in anomaly detection. Therefore

the precise estimation of background statistic model is a current research direction. This study provides a novel means of obtaining the model. The novel clustering algorithm combined with the iteration process can extract an efficient uncontaminated model. The proposed method can certainly be improved. The largest population cluster obtained in each iteration step is gathered as the final statistic model. We can also consider clusters independently and analyze the background features under each category. The parameters in this study are determined by experience and experiments. The experiments show that all parameters result in local optimum. Thus

we must develop methods for establishing models for finding global optimal parameters. Furthermore

time consumption requires improvement. The algorithm steps can be further optimized to reduce the time consumption. These steps will be our next research direction.