Topic: Anomaly Detection from Hyperspectral Remote Sensing Imagery

Next in our spring seminar series, we welcome Dr. Jenny Du, a professor from Mississippi State University.

Abstract:
Anomaly detection from remote sensing images is to detect pixels whose spectral signatures are different from their background. Anomalies are often man-made targets. With such target signatures being unknown, anomaly detection has many important applications, such as crop stress surveying, water quality monitoring, and law enforcement related uses, where prior information of targets is often unavailable. The key to success is accurate background modeling. Anomaly detection from remote sensing images is challenging, because spatial coverage is very large and background is highly heterogeneous. Hyperspectral imaging with very high spectral resolution offers the advantage in background-anomaly separation. In this talk, the major machine learning techniques in anomaly detection from hyperspectral remote sensing images will be introduced, and the recent advances using transfer learning and deep learning for anomaly detection will be presented. In particular, autoencoder, generative adversarial network, and adversarial autoencoder are deployed and compared with traditional approaches. Practical issues and future development trends will also be discussed.

You can see the presentation live at the UTSA Main Campus in the Student Union Mesquite room (2.01.24).

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/94807623288.