Research On Hyperspectral Target Detection Algorithm Based On Background Suppressio

Hyperspectral image target detection is one of the important methods of space earth observation,and it is also a key research field of scholars at home and abroad.As the technology continues to mature,efficient and stable results can be obtained for typical target detection using the rich information provided by hyperspectral images.However,in practical applications,conditions such as wide image scenes,sparse targets,and complex background objects often cause interference to detection.However,the existing target detection algorithms often cannot fully solve these problems,and there is also a lack of a complete and stable detection model for the target detection of typical ground objects.Aiming at the above problems,this paper proposes a hyperspectral target detection algorithm based on background suppression,and designs a set of target detection process from area to target.Facing the target detection requirements of typical ground objects in large-scale hyperspectral images,and aiming at the problem of high false alarms caused by complex scenes and large amounts of information in hyperspectral target detection,this paper proposes a target area detection algorithm based on saliency analysis.The algorithm makes full use of the characteristics of the target area,and obtains the target area position of the image through the improved saliency analysis model,thereby reducing the target detection range and significantly reducing the complexity of subsequent target detection.The experimental results show that the algorithm has a stable detection effect,solves the problem of high false alarms caused by complex backgrounds,and can obtain better detection performance in different types of complex backgrounds.Compared with other algorithms,it has better detection accuracy and speed.a certain improvement.After solving the large-area background redundancy through target area detection,and aiming at the problems of inaccurate estimation of background statistical information and low utilization rate in traditional target detection algorithms,this paper proposes a hyperspectral target detection algorithm based on background suppression collaborative representation.The algorithm achieves background suppression through the outlier removal strategy,and substitutes the suppressed background dictionary into the collaborative representation algorithm,and then uses multiple weight constraints to suppress the background pixels,increasing the difference between the target pixel and the background pixel,thereby improving the detection effect.Experimental results show that the algorithm can effectively reduce the impact of background pixels on target detection,and still has better detection performance than the recently proposed algorithm.In addition,combined with the target area detection algorithm proposed in this paper,the overall process of hyperspectral image target detection in complex scenes can be improved.Finally,the self-made data set is used to verify the effectiveness of the overall algorithm,which proves that the algorithm in this paper can effectively improve the target detection rate,so that it is more suitable for the target detection of hyperspectral images in large scenes.