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

Jhao Ting Lin, Chia Hsiang Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1752-1755
Number of pages4
ISBN (Electronic)9781665427920
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
Duration: 2022 Jul 172022 Jul 22

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2022-July

Conference

Conference2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period22-07-1722-07-22

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • General Earth and Planetary Sciences

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