TY - JOUR
T1 - Multiple Features and Isolation Forest-Based Fast Anomaly Detector for Hyperspectral Imagery
AU - Wang, Rong
AU - Nie, Feiping
AU - Wang, Zhen
AU - He, Fang
AU - Li, Xuelong
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - Hyperspectral anomaly detection (HAD) has drawn a significant attention of late due to its importance in many military and civilian applications. In this article, a fast hyperspectral anomaly detector that combines multiple features and isolation forest is proposed. This approach, which is based on the assumption that the anomalous pixels are more susceptible to isolation than the background pixels, consists of two main parts. First, the spectral, Gabor, extended morphological profile (EMP) and extended multiattribute profile (EMAP) features are extracted from the hyperspectral image (HSI). Next, the isolation forest of each feature is constructed using the subsampling strategy. This combination of multiple features can exploit both the spectral and spatial information of the HSI, thereby improving the anomaly detection performance significantly. Compared with eight state-of-the-art HAD methods, the experimental results on four real hyperspectral data sets demonstrate that the performance of our proposed approach is quite competitive in terms of detection accuracy and running time.
AB - Hyperspectral anomaly detection (HAD) has drawn a significant attention of late due to its importance in many military and civilian applications. In this article, a fast hyperspectral anomaly detector that combines multiple features and isolation forest is proposed. This approach, which is based on the assumption that the anomalous pixels are more susceptible to isolation than the background pixels, consists of two main parts. First, the spectral, Gabor, extended morphological profile (EMP) and extended multiattribute profile (EMAP) features are extracted from the hyperspectral image (HSI). Next, the isolation forest of each feature is constructed using the subsampling strategy. This combination of multiple features can exploit both the spectral and spatial information of the HSI, thereby improving the anomaly detection performance significantly. Compared with eight state-of-the-art HAD methods, the experimental results on four real hyperspectral data sets demonstrate that the performance of our proposed approach is quite competitive in terms of detection accuracy and running time.
KW - Hyperspectral anomaly detection (HAD)
KW - hyperspectral image (HSI)
KW - isolation forest
KW - multiple features
UR - http://www.scopus.com/inward/record.url?scp=85088903388&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2020.2978491
DO - 10.1109/TGRS.2020.2978491
M3 - 文章
AN - SCOPUS:85088903388
SN - 0196-2892
VL - 58
SP - 6664
EP - 6676
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 9
M1 - 9040873
ER -