Next Article in Journal
The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors
Next Article in Special Issue
Band Subset Selection for Hyperspectral Image Classification
Previous Article in Journal
Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm
Previous Article in Special Issue
Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(1), 103; doi:10.3390/rs10010103

Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

1
Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou 310018, China
2
Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian 116026, China
3
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Received: 29 November 2017 / Revised: 6 January 2018 / Accepted: 9 January 2018 / Published: 13 January 2018
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
View Full-Text   |   Download PDF [2146 KB, uploaded 17 January 2018]   |  

Abstract

Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, the W o o d b u r y identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors. View Full-Text
Keywords: hyperspectral imagery; recursive anomaly detection; local summation RX detector (LS-RXD); sliding window hyperspectral imagery; recursive anomaly detection; local summation RX detector (LS-RXD); sliding window
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Zhao, L.; Lin, W.; Wang, Y.; Li, X. Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows. Remote Sens. 2018, 10, 103.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top