After fusing SECM images and constructing training data set, we used the CNN model to process image classification. CNN is a widely used model in image classification; it's a model that uses multiple filters as sliding windows that scan around to extract local features and construct a higher-level abstract concept. In other words, it collects lots of different small patterns and maps them into the whole image to build feature maps. And some hidden features between data are therefore distilled in this process through these layer structures, which makes it powerful for spatial feature extraction. CNN is usually composed of several convolution layers and pooling layers.