## 1 Introduction  
Deep learning is an increasingly popular approach in remote sensing classification. While traditional remote sensing classification methods require users to manually design features, deep learning, a new branch of machine learning, provides an effective framework for the automatic extraction of features [1, 2]. Deep learning algorithms include supervised and unsupervised learning algorithms; a convolutional neural network (CNN) is a supervised deep learning algorithm, while an auto-encoder is a typical unsupervised learning algorithm. CNNs are typically applied in remote sensing classification efforts in two ways: pixel-based classifications [3, 4, 5] and scene classifications [6, 7]. While there are only a few studies that address object-oriented remote sensing classification based on CNNs, the first-place winner of Dstl's Satellite Imagery competition, [PERSON], proposed a new, improved U-Net model to identify and label significant objects using satellite imagery. However, compared to the object-based classification method, object boundaries identified using this approach are imprecise [8, 9]. Object-oriented remote sensing classification not only takes into account the spectral information of objects but also considers the statistical, shape, texture, etc., information, which has helped improve classification accuracy [10] and is gaining increased attention from many researchers. However, problems with the application of deep CNNs in object-oriented remote sensing classification efforts still exist. Varying sources, formats, and uses of remote sensing data result in the following issues: (1) The structures of most common CNNs are complex, which requires high computation complexity and a large number of samples, and the CNNs will be prone to over-fitting problems when the number of labeled samples is limited. (2) It is difficult to obtain public remote sensing datasets. Different remote sensing images have different spatial resolutions. Each pixel has its own semantics, which corresponds to a different category, and the scale effect is more prominent when applied to object-based analysis. (3) There is no universal CNN for object-oriented remote sensing classification. Unlike the standard RGB images processed using pattern recognition in the computer field, remote sensing images involve different scales and semantics, different users have different requirements, and classification standards are based on the application's needs, so it is unrealistic to generate a universal CNN for different remote sensing images. Therefore, it is necessary to replace the complicated structure of existing deep neural networks with a simple and efficient network structure for different remote sensing data, further reduce the network's complexity, and improve the performance of the network to improve its application. To solve the above problems, a hybrid deep neural network model based on a convolutional auto-encoder (CAE) and a CNN is proposed. First, the CAE is used to compress the data and eliminate any redundancies in the original image while preserving the original features. Then, the multidimensional feature maps are extracted by the CAE and replace the original image as the input for the CNN, which continues the classification process. Compared to the pre-training weight approach for CNNs proposed by [PERSON] et al. [11], in the proposed model the classification features can be more pertinently trained. The experiment shows that, on the one hand, the method can simplify the structure of the CNN, and on the other hand, it can reduce the parameters generated from 5780 to 4247, while the fully-connected parts are similar and the convolution parts are reduced from 2895 to 1242, thereby improving the temporal efficiency and overall accuracy of the CNN in object-oriented remote sensing classification applications and reducing its dependence on the number of labelled samples. The rest of the paper is organized as follows.