A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters
A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy.
Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters
A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy.
Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters
Kang, Xudong (author) / Zhang, Xiangping / Li, Shutao / Li, Kenli / Li, Jun / Benediktsson, Jon Atli
2017
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
Spectral-Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
| Online Contents | 2014
Spectral-Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
| Online Contents | 2014
PCA-Based Edge-Preserving Features for Hyperspectral Image Classification
| Online Contents | 2017
Collaborative Representation for Hyperspectral Anomaly Detection
| Online Contents | 2015
Classification and Hyperspectral - Anomaly Detection and Classification for Hyperspectral Imagery
| Online Contents | 2002