LAUSR

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 article. 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 AD 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 dataset, 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 datasets 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.