Efficient target detection by object-based thematic mapping using remote sensing imagery

The detection of objects from a cluttered background using remote sensing data may cause many false alarms if the target object and the background have overlapping spectra. In this study, we propose an integrated approach to combine pixel-based spectral labeling with object-based spatial property measures. A hierarchical structure is developed in which multileveled attributions and decision rules can be implemented. The targets are then extracted progressively. Experimental results show a substantial reduction in the number of false alarms with the proposed method.

[1]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[2]  David Tien,et al.  Compound Geospatial Object Detection in an Aerial Image , 2007, VISUAL.

[3]  P. Swain,et al.  Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data , 1990 .

[4]  John A. Richards,et al.  A Dempster–Shafer Relaxation Approach to Context Classification , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[5]  G. Mercier,et al.  Support vector machines for hyperspectral image classification with spectral-based kernels , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[6]  L.L. Scharf,et al.  Adaptive matched subspace detectors and adaptive coherence estimators , 1996, Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers.

[7]  Tatsuya Yamazaki,et al.  Image classification using spectral and spatial information based on MRF models , 1995, IEEE Trans. Image Process..

[8]  E. J. Kelly.  An Adaptive Detection Algorithm , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[9]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[10]  Russell M. Mersereau,et al.  On the impact of PCA dimension reduction for hyperspectral detection of difficult targets , 2005, IEEE Geoscience and Remote Sensing Letters.

[11]  David A. Landgrebe,et al.  Signal Theory Methods in Multispectral Remote Sensing , 2003 .

[12]  Daniel R. Fuhrmann,et al.  A CFAR adaptive matched filter detector , 1992 .

[13]  Louis L. Scharf,et al.  Matched subspace detectors , 1994, IEEE Trans. Signal Process..

[14]  Chein-I Chang,et al.  Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..

[15]  W. Farrand.  Mapping the distribution of mine tailings in the Coeur d'Alene River Valley, Idaho, through the use of a constrained energy minimization technique , 1997 .

[16]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .