在复杂多变的海面环境下，应用红外成像技术对海面中小目标进行搜救时，为有利于后续针对不同场景的目标处理，有必要对采集的原始图像进行分类处理。根据不同的环境条件，将海面红外图像分为五类场景。从两个方面对训练集图像进行特征提取，一个是通过高斯滤波将图像分为基础层和细节层，然后使用改进的方向梯度直方图（HOG）方法提取特征；另一个是提取图像的局部对比度得到局部特征。将提取的特征向量融合并输入到分类器中，使用支持向量机（SVM）对测试集图像进行分类。文章使用了HOG和局部对比度方法（LCM）结合的新特征描述符对海面红外图像的场景进行分类，与其它方法相比，结果表明改进方法的准确率达到96.4%，体现了可行性和有效性。

In the complex and changeable sea environment， when using infrared imaging technology to search and rescue small and medium targets on the sea surface， it is necessary to classify the collected original images in order to facilitate the subsequent target processing in different scenes. According to different environmental conditions， the sea infrared images are divided into five kinds of scenes. The training set images are extracted from two aspects： one is to divide an image into basic layer and detail layer by the Gaussian filter， and use improved histogram of oriented gradient （HOG） method to extract the features； the other is to extract features by calculating local contrast of images. The extracted feature vectors are fused and input into the classifier， and the test set images are classified by support vector machine （SVM）. In this paper， a new feature descriptor combined with HOG and local contrast method （LCM） is used to classify the scene of sea infrared image. Compared with other methods， the results show that the accuracy of the improved method is 96.4%， which reflects the feasibility and effectiveness.