Hyperspectral Anomaly Detection via Spatial Density Background Purification

Abstract

In the research of anomaly detection methods, obtaining a pure background without abnormal pixels can effectively improve the detection performance and reduce the false-alarm rate. Therefore, this paper proposes a spatial density background purification (SDBP) method for hyperspectral anomaly detection. First, a density peak clustering (DP) algorithm is used to calculate the local density of pixels within a single window. Then, the local densities are sorted into descending order and the m pixels that have the highest local density are selected from high to low. Therefore, the potential abnormal pixels in the background can be effectively removed, and a purer background set can be obtained. Finally, the collaborative representation detector (CRD) is employed for anomaly detection. Considering that the neighboring area of each pixel will have homogeneous material pixels, we adopt the double window strategy to improve the above method. The local densities of the pixels between the large window and the small window are calculated, while all pixels are removed from the small window. This makes the background estimation more accurate, reduces the false-alarm rate, and improves the detection performance. Experimental results on three real hyperspectral datasets such as Airport, Beach, and Urban scenes indicate that the detection accuracy of this method outperforms other commonly used anomaly detection methods.

A Hyperspectral Anomaly Detection Algorithm Based on Morphological Profile and Attribute Filter with Band Selection and Automatic Determination of Maximum Area

“…The performance of MPAF was evaluated with the most widely used metrics, area under the curve (AUC), and the receiver operating characteristic (ROC) [44]. To verify the performance of the proposed method, other methods, i.e., RX [20], FrFE-RX [24], AED [33], and SDBP-D [29] (SDBP with dual window) detectors, were used for comparison. These methods were either frequently cited in the literature or recent algorithms for HSI anomaly-detection applications.…”

“…The other parameters of AED were kept at AED's default parameter setting, i.e., λ = N/100, σ s = 5, σ s = 0.5, and M = 3. Similarly, for SDBP-D, we used the values recommended in [29] to set the window size (ω in and ω out ) and the percentage of the highest selected density value (P).…”

“…In order to address this issue, variants of CRD were proposed [26][27][28]. A recent algorithm, called spatial density background purification (SDBP) [29], also aimed to solve this problem. It used a density peak (DP) clustering algorithm to calculate the local density of the neighborhoods and remove some of the neighborhoods that have a low density to acquire pure background pixels in the neighborhoods.…”

Modified Autoencoder Training and Scoring for Robust Unsupervised Anomaly Detection in Deep Learning

“…All scenes fill a 100 × 100 pixel area with the exception of beach-1, which spans 150 × 150 pixels. We refer to Tu et al for more details on the spatial resolution and location of each scene [48].…”

Random Collective Representation-Based Detector with Multiple Features for Hyperspectral Images

“…These techniques make use of the conspicuous characteristics of anomalies: the low probability of occurrence and the different spectral signature from the background pixels [27]. There are several forms of representation-based methods, including the sparse representation-based methods [1,[31][32][33][34][35][36][37], the low-rank methods [38][39][40][41][42][43], and the collaborative methods [44][45][46][47][48].…”