Hyperspectral image is a three-dimensional data cube which describes spatial information and spectral information of the scene. The anomaly detection technique can detect the targets which have difference between the image and the background without priori information. Kernel independent component analysis(KICA) is a method of mapping hyperspectral data into the kernel space for feature extraction. In this paper, the hyperspectral image is subjected to abnormal information detection based on KICA. First, we calculate the kernel matrix K in order to map the data to high-dimensional space for whitening and dimension reduction processing. Then we utilize the FastICA algorithm to extract the core independent component (KIC). Finally, the extracted independent components with the most abnormal information are analyzed by RX operator, kernel RX operator and abundance quantization method. Comparing with the simulation result and the detected result by RX method, the representation shows the algorithm based on KICA has better detection performance.
It is very critical that make full use of the local information for infrared dim and small target tracking. In this paper, an effective and fast algorithm based on the context learning is proposed to track infrared dim moving target. Firstly, the principle of the spatio-temporal context learning algorithm is described and the tracking deviation is analyzed. Then, a correlation filter is utilized to get a rational context prior for the dim moving target, the advantage is that the prior considers the image intensity information between a target and its surround pixels. Furthermore, a Gaussian high-pass filter is introduced to extract an accurate spatial context, which has little influence caused by the cluttered background. At last, the tracking problem is posed by computing a confidence map which takes into account sufficient information of a dim target and its surround background. Since the proposed algorithm is realized using fast Fourier transform, it is easy to be real-time. The experiments on various clutter background sequences have validated the tracking capability of the proposed method. The experimental results show that the proposed method can provide a higher accuracy and speed than several classical algorithms, including the improved Template Matching algorithm, the Temporal-Spatial Fusion Filtering algorithm and the Moving Pipeline Filtering algorithm.
The drawback of temporal high-pass non-uniformity correction algorithm, ghosting and the image blurring, severely degrades the correction quality. In this paper, an improved non-uniformity correction algorithm based on shearlet transform is proposed. First, the proposed algorithm decomposes the original infrared image into one low frequency sub-band and a group of high frequency sub-bands by the shearlet transform. As a powerful mathematical tool, the decomposition of image by shearlet can reveal the detail of the image accurately. As the high frequency sub-bands contain the most of FPN, the FPN is estimated from the high frequency sub-bands by temporal high-pass. Then, the goal of non-uniformity correction can be achieved by subtracting the estimated FPN from the original high frequency sub-bands. At last, the corrected infrared image can be obtained by the inverse shearlet transform. The performance of the proposed algorithm is thoroughly studied with real infrared image sequences. Experimental results indicate that the proposed algorithm can reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.
An infrared dim and small tracking is proposed based on an explicit image filter - guided filter. The guided filter utilizes the structure in the guidance image and performs as an edge-preserving smoothing operator. The superior performance depending on the guidance image is critical advantage for target tracking. First, the guided filter can help to preserve the detail of the valuable templates and make the inaccurate ones blurry so that the tracker can distinguish the target from numerous bad templates easily. Besides, the filter can recover the content of the small target being influenced according to the guidance image, helping to alleviate the drifting problem effectively. Finally, the candidate samples are utilized to train an effective Bayes classifier to generate a robust tracker, which is easy to be implemented. Experimental results demonstrate that the presented method can track the target effectively, compared with several classical methods. Experimental results show that the proposed algorithm outperforms relative trackers in the accuracy and the robustness.
In this paper, a curvature filter and PDE based non-uniformity correction algorithm is proposed, the key point of this algorithm is the way to estimate FPN. We use anisotropic diffusion to smooth noise and Gaussian curvature filter to extract the details of original image. Then combine these two parts together by guided image filter and subtract the result from original image to get the crude approximation of FPN. After that, a Temporal Low Pass Filter (TLPF) is utilized to filter out random noise and get the accurate FPN. Finally, subtract the FPN from original image to achieve non-uniformity correction. The performance of this algorithm is tested with two infrared image sequences, and the experimental results show that the proposed method achieves a better non-uniformity correction performance.
The target is moving and changing in infrared image sequences captured from the airborne platform infrared imaging system. To adaptively track the infrared target which changes from small target to surface target, an algorithm based on Second-Order Differential (SOD) and improved Template Matching (TM) tracking algorithm was proposed. The SOD filter makes full use of the brightness of the infrared dim and small target, the gradient and distance information of neighborhood pixels used for spatial domain filter. The TM makes full use of infrared brightness, ambient background and dimension information to complete the tracking. The experimental results show that the proposed algorithm can convert adaptively with infrared target’s size changing information, so tracking stability of infrared target under the ground clutter background is achieved. The tracking accuracy and tracking speed are also better than traditional algorithms. The proposed algorithm can be well applied to airborne platform warning on the ground.
The target tracking by the spatio-temporal learning is a kind of online tracking algorithm based on Bayesian framework. But it has the excursion problem when applied in the infrared dim target. Based on the principle of the spatio-temporal learning algorithm, the excursion problem was analyzed and a new robust algorithm for infrared dim target tracking is proposed in this paper. Firstly, the Guide Image Filter was adopted to process the input image to preserve edges and eliminate the noise of the image. Secondly, the ideal spatial context model was calculated with the input image that contains little noise, which can be got by subtracting the filtering result from the original image. Simultaneously, a new weight in the context prior model was proposed to indicate that the prior is also related to the local gray level difference. The performance of the presented algorithm was tested with two infrared air image sequences, and the experimental results show that the proposed algorithm performs well in terms of efficiency, accuracy and robustness.
A core technology in the infrared warning system is the detection tracking of dim and small targets with
complicated background. Consequently, running the detection algorithm on the hardware platform has highly practical
value in the military field. In this paper, a real-time detection tracking system of infrared dim and small target which is
used FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) as the core was designed and the
corresponding detection tracking algorithm and the signal flow is elaborated. At the first stage, the FPGA obtain the
infrared image sequence from the sensor, then it suppresses background clutter by mathematical morphology method and
enhances the target intensity by Laplacian of Gaussian operator. At the second stage, the DSP obtain both the original
image and the filtered image form the FPGA via the video port. Then it segments the target from the filtered image by an
adaptive threshold segmentation method and gets rid of false target by pipeline filter. Experimental results show that our
system can achieve higher detection rate and lower false alarm rate.
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