Since the intrusion detection method based on One-Class Support Vector Machine (OCSVM) can not detect internal abnormal points and outliers, which leads to the deviation of decision function from training samples. A new OCSVM anomaly detection function combining DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and K-means was proposed. Firstly, the outliers in the training data were removed by DBSCAN algorithm to eliminate the influence of outliers. Then, K-means clustering method was used to classify normal data clusters, so that the internal abnormal points could be selected. Finally, a one-class classifier for each data cluster was created to detect exception data by OCSVM algorithm. The experimental results on industrial control networks show that the combined classifier can detect the intrusion attacks of the industrial control network by using normal data, and it can improve the detection effect of OCSVM algorithm. In intrusion detection experiment of gas pipeline, the overall detection rate of the proposed method is 91.81%, while the overall detection rate of OCSVM algorithm is 80.77%.

When using Linear Deconvolution (LD) algorithm in the selection process, endmembers subset has similar endmembers and similar endmembers have an impact on the accuracy of spectral unmixing,a hyperspectral unmixing optimization algorithm based on per-pixel optimal endmember selection named Spectral Information Divergence (SID) and Spectral Angle Mapping (SAM) was proposed. At the end of the second choice, the method adopted Spectral Information Divergence mixed with Spectral Angle (SID-SA) rule as the most similar endmember selection criteria, removed the similar endmembers and reduced the effect of the accuracy by spectral unmixing. The experiment results show that hyperspectral unmixing optimization algorithm based on SID and SAM makes Root Mean Square Error (RMSE) of reconstruction images be reduced to 0.0104. This method improves the accuracy of endmember selection in comparison with traditional method, reduces abundance estimation error and error distributes more evenly.

There is a long time detection problem caused by too large surveyed area in the classical Tracking-Learning-Detection (TLD) target tracking algorithm. Moreover, the TLD algorithm could not do the similar targets processing well. So in this paper, an efficient approach called TLD-DO was proposed for tracking targets in which the surveyed areas could be automatically adjusted according to the target's velocity of movement. In order to accelerate the process speed of TLD algorithm without reducing tracking precision, a novel algorithm named Double Kalman Filter (DKF) with optimal surveyed area which could reduce the detection range of TLD detector was constructed based on twice Kalman filtering operation for acceleration correction. Meanwhile, the improved method could also increase the accuracy of target tracking through eliminating the interference of the similar targets in complex background. The experimental results show that tracking effect of improved method is better than that of the original TLD algorithm under the circumstance of similar target disturbance. Furthermore, the detection speed has been improved 1.31-3.19 times for different videos and scenes. In addition, the improved method is robust to target vibration or distortion.