When using the Bscan image generated by Ground Penetrating Radar （GPR） to detect underground targets， the current target detection network models based on deep learning have some problems， such as high demand of training samples， long time consuming， unable to distinguish the significance of targets， and difficult to identify complex targets. To solve the above problems， a double threshold segmentation algorithm based on histogram was proposed. Firstly， based on the distribution characteristics of GPR image histogram of underground target， two thresholds for underground target segmentation were calculated quickly from the histogram. Then， a combination classifier model with Support Vector Machine （SVM） and LeNet was used to classify the segmentation results. Finally， classification results were integrated and the accuracy values were counted. Compared with the traditional threshold segmentation algorithms such as Ostu and iterative methods， the structure of the underground target segmentation results obtained by the proposed algorithm was more complete and almost free of noise. On the real dataset， the average recognition accuracy of the proposed algorithm reached more than 90%， which was more than 40% higher than that of the algorithm using a single classifier. The experimental results show that the salient and non-salient underground targets can be effectively segmented at the same time， and the combination classifier can obtain better classification results. It is suitable for automatic detection and recognition of underground targets with small sample datasets.