Real-Time Hyperspectral Anomaly Detection using Collaborative Superpixel Representation with Boundary Refinement

Jhao Ting Lin, Chia Hsiang Lin

研究成果: Conference contribution

摘要

Hyperspectral anomaly detection (HAD) is a crucial task that aims to classify the given image into abnormal pixels and background pixels. Besides, the classification boundary between the abnormal pixels and the background pixels is implicit, making HAD a challenging problem. An existing method for anomaly detection is proposed based on collaborative representation. Since the method performs the detection on each pixel, it is not computationally efficient. To reduce the computational cost, we develop a new method based on collaborative representation. First, superpixel segmentation is utilized to cluster the image. Then, we perform the collaborative representation on each superpixel to obtain a rough detection result. According to the preliminary result, a threshold is automatically calculated to classify potential abnormal superpixels and background superpixels. At last, the boundaries of abnormal superpixels are refined to yield a more accurate detection result. In the real data experiments, we show that our method has satisfactory visual qualities and state-of-the-art quantitative performance.

原文English
主出版物標題IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1752-1755
頁數4
ISBN(電子)9781665427920
DOIs
出版狀態Published - 2022
事件2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
持續時間: 2022 7月 172022 7月 22

出版系列

名字International Geoscience and Remote Sensing Symposium (IGARSS)
2022-July

Conference

Conference2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
國家/地區Malaysia
城市Kuala Lumpur
期間22-07-1722-07-22

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 地球與行星科學(全部)

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