Anomaly Detection Based on Tree Topology for Hyperspectral Images

Abstract

As one of the most important research and application directions in hyperspectral remote sensing, anomaly detection (AD) aims to locate objects of interest within a specific scene by exploiting spectral feature differences between different types of land cover without any prior information. Most traditional AD algorithms are model-driven and describe hyperspectral data with specific assumptions, which cannot combat the distributional complexity of land covers in real scenes, resulting in a decrease in detection performance. To overcome the limitations of traditional algorithms, a novel tree topology-based anomaly detection (TTAD) method for hyperspectral images (HSIs) is proposed in this paper. TTAD departs from the single analytical mode based on specific assumptions but directly parses the HSI data itself. It makes full use of the "few and different" characteristics of anomalous data points that are sparsely distributed and far away from high-density populations. On this basis, topology, a powerful tool in mathematics that successfully handle multiple types of data mining tasks, is applied to anomaly detection to ensure sufficient feature extraction of land covers. First, the redistribution of HSI data is realized by constructing a tree-type topological space to improve the separability between anomalies and backgrounds. Then, topologically related subsets in this space are utilized to evaluate the abnormality degree of each sample in a data set, and detection results for the HSI are output accordingly. Abandoning traditional modeling but focusing on mining the data characteristics of HSI itself enables TTAD to better adapt to different complex scenes and locate anomalies with high precision. Experimental results on a large number of benchmark data sets demonstrate that TTAD could achieve excellent detection results with considerable computational efficiency. The proposed method exhibits superior comprehensive performance and is promising to be popularized in practical applications.

Hyperspectral anomaly detection via weighted‐sparsity‐regularized tensor linear representation

“…In ref. [31], a tree‐type topological space was also utilized to enhance the discriminability. Once the topological space constructed, the topological cardinality can be utilized to measure the abnormality of the samples.…”

Kernel Minimum Noise Fraction Transformation-Based Background Separation Model for Hyperspectral Anomaly Detection

“…With the characteristic of high spectral resolution, hyperspectral images (HSIs) reveal an enormous number of details about the spectral features of the Earth's surface and have unique advantages in various applications such as spectral unmixing, classification, and anomaly detection (AD) [1][2][3][4][5][6]. Among these applications, AD is usually treated as detecting anomalies by referring to a background model and has attracted much attention because of its importance in civilian and military applications [7][8][9][10][11]. It usually possesses the following characteristics: (1) there is no prior spectral information about the anomalies or background; (2) anomalies are different from the background in terms of spectral signatures; and (3) anomalies are small objects that occupy a relatively small part of the image [12][13][14].…”