1. School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
2. School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China
3. The Fifty-five Research Institute of China North Industries Group, Changchun 130012, China
Abstract：Hyperspectral imaging can provide multi-dimensional reference information for the target to be classified by its high spectral resolution, spectrum integration and multiple bands, thus improving the classification accuracy. The identification and recovery of explosive fragments can provide a reference for evaluating explosive power and designing explosion-proof measures. Because of the current fragment detection, single bands such as visible light band or infrared band are mostly used for detection, ignoring that the fragment target and background have different degrees of absorption of light of different wavelengths and do not make full use of the characteristics of multi-band fragments. Therefore, this paper combines hyperspectral detection means and proposes a method of explosive fragment recognition based on spatial segmentation and spectral information. In the laboratory environment, first collect the hyperspectral images of iron fragments, rocks and leaves, and preprocess the collected sample image data, including noise removal and black and white correction to retrieve the reflectivity information. Randomly extract 750 sample pixels of three types from the region of interest, and randomly select 600 points as the rest of the training set as the test set. After training, a decision tree model with a prediction accuracy of 88%, 88% and 94% is obtained. Secondly, the scene of iron fragments scattered in the sand with stone leaves is simulated, and its hyperspectral data is collected. Through the cascade space spectrum fusion method, after image enhancement and denoising in the spatial domain, the spatial image is segmented using edge detection combined with region growth and morphological processing methods to obtain the morphological targets on the sand. The intersection and union ratio (IOU) of spatial segmentation reaches 93.5%, and the true positive rate (TPR) reached 97.4%; Then, combined with the decision tree model trained in the spectral domain, each pixel point in each segmentation area is identified in the spectral domain. The number three types of pixel points involved in classification are 146 172, 50 484, 213 438, and the recognition accuracy is 87%, 86%, 96% respectively; Finally, the classification results are visualized. The category with the largest number of pixels in each region represents the category of the region. The target fragments and the two backgrounds of stones and leaves are accurately recognized. With the calibrated segmented image as the standard, the number of pixels in the three categories is 155 502, 52 045, 217 794, and the recognition rate is 94%, 97%, and 98% respectively. The analysis results show that the recognition method of spatial segmentation combined with spectral information can effectively use spatial and hyperspectral feature information to identify iron fragment targets accurately. At the same time, it also verified the scientificity and feasibility of using hyperspectral imaging to conduct space spectrum joint identification of explosive fragments, which has a certain practical significance for the future assessment of fragment warhead power using intelligent fragment identification.
Key words：Hyperspectral imaging; Spatial segmentation; Decision trees; Iron fragments; Classification and identification
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