Distributed fiber-optic vibration sensors receive extensive investigation and play a significant role in the sensor panorama. A fiber optic perimeter detection system based on all-fiber interferometric sensor is proposed, through the back-end analysis, processing and intelligent identification, which can distinguish effects of different intrusion activities. In this paper, an intrusion recognition based on the auditory selective attention mechanism is proposed. Firstly, considering the time-frequency of vibration, the spectrogram is calculated. Secondly, imitating the selective attention mechanism, the color, direction and brightness map of the spectrogram is computed. Based on these maps, the feature matrix is formed after normalization. The system could recognize the intrusion activities occurred along the perimeter sensors. Experiment results show that the proposed method for the perimeter is able to differentiate intrusion signals from ambient noises. What’s more, the recognition rate of the system is improved while deduced the false alarm rate, the approach is proved by large practical experiment and project.
All-fiber interferometer sensor system is a new type of system, which could be used in long-distance, strong-EMI condition for monitoring and inspection. A fiber optic perimeter detection system based on all-fiber interferometric sensor is proposed, through the back-end analysis, processing and intelligent identification, which can distinguish effects of different intrusion activities. In this paper, the universal steps in triggering pattern recognition is introduced, which includes signal characteristics extracting by accurate endpoint detecting, templates establishing by training, and pattern matching. By training the samples acquired in the laboratory, this paper uses the wavelet transformation to decompose the detection signals of the intrusion activities into sub-signals in different frequency bands with multi-resolution analysis. Then extracts the features of the above mentioned intrusions signals by frequency band energy and wavelet information entropy and the system could recognize the intrusion activities occurred along the perimeter sensors. Experiment results show that the proposed method for the perimeter is able to differentiate intrusion signals from ambient noises such as windy and walk effectively. What’s more, the recognition rate of the system is improved while deduced the false alarm rate, the approach is proved by large practical experiment and project.
The multi-sensor image fusion technology can obtain a more comprehensive and more accurate and reliable image, in
order to understand the scene or recognize the target more easily. However, most existing algorithms are mainly based on
optical remote sensing images, which is highly susceptible by media interference, supplemented by SAR images. The
image fusion between SAR images and PAN images also cannot save the textural feature and the color information
effectively at the same time. In view of these problems, this paper presents a multi-sensor image fusion algorithm based
on region-based selection and IHS transform. The SAR image and PAN image are firstly IHS transformed to achieve the
intensity (I), hue (H) and saturation (S) weights. The I weights of SAR image and PAN image are separately decomposed
using SIDWT algorithm to extract wavelet coefficients. Then, the I weight of SAR image is divided into regular area and
irregular area based on a new adaptive segmentation method. A new fusion rules is presented according to local feature,
and then used to fuse corresponding wavelet coefficients of the I weight of SAR image and PAN image. Inverse SIDWT
is carried out on the fused wavelet coefficients to get the I weight (I’) of fused image. Finally, the fused image is obtained
by inverse IHS transform of I’ weight with the H, S weight of PAN image. Experimental results of real images validated
the effectiveness of the proposed algorithm by objective evaluation such as standard deviation, entropy, average gradient,
Proc. SPIE. 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
KEYWORDS: Target detection, Information fusion, Eye, Visual process modeling, Detection and tracking algorithms, Remote sensing, Biomimetics, Data processing, Mahalanobis distance, Information operations
Aimed to the limitation of present anomaly detection algorism under clutter background for multi-spectral remote sensing data, especially for the situations of dense spread target and exist different attributive of background objects, a bio-inspired anomaly detection algorithm was proposed. Simulate the information processing and fusion mechanism of fly multi-apertures vision system, multi-level background model was proposed to analysis and describe feature of clutter background. Then the threshold value can be chose adaptively according to the level of background model. The proposed algorithm didn’t need the prior knowledge about anomaly, and avoids the choosing of the background widow size. A fusion mechanism was proposed to fuse the different detection results with different level background model. Simulation experiment validated the effectiveness of proposed method.