Graphical Topology Autonomic Identification Method for Human-Machine Interface Modelling Based on Quadratic Space Mapping

Wu, Yanjuan; Wang, Haoyue; Wang, Yunliang; Li, Yang

doi:10.1109/access.2020.3001189

Cited by 2 publications

(1 citation statement)

References 33 publications

(42 reference statements)

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“…Based on such fact, topology, a powerful mathematical tool, is adopted to solve the data mining task of anomaly detection. Topology has been proved to be capable of solving various types of tasks in machine learning involving point set analysis, including anomaly detection, information retrieval, classification [53], [54], etc. The general idea of its implementation in these tasks is to map the research object into a certain number of point sets, whose relations are represented by a geometric space to achieve an evaluation purpose associated with the task requirements.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Based on Tree Topology for Hyperspectral Images

Sun

Zhang

Zhuang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%