The deep learning method exhibits high performance as a large data autosorting tool. However, when dealing with remote sensing image tasks, such as image classification, the problem of low efficiency is shown. Therefore, a new classification algorithm for remote sensing image based on local classifier and deep neural network is proposed in this paper. First, the method extracts a plurality of local features from the original image and inputs them into the deep neural network for the decision, and then classifies each local feature according to the assignment to the image tag. Finally, the result of the overall image is judged according to the simple voting method. Based on the WorldView2 high-resolution satellite remote sensing image data, the classification experiment was carried out. Experimental results show that the proposed method is superior to other classification methods and it has better classification accuracy and classification efficiency.