## 2 Related Work  
This paper involves the following aspects: object-oriented remote sensing classification, network structure optimization, and CNNs and CAEs in the application of remote sensing classification. Object-oriented remote sensing classification: The \"salt and pepper phenomenon\", which occurs often when using the pixel-based classification method and results in low classification accuracy, has been common since high-resolution satellite imagery was developed. Object-oriented remote sensing classification methods emerged to overcome this phenomenon [10]. However, traditional object-oriented classification methods that depend on artificially designed features have many disadvantages. At least 150 kinds of spectral, texture, shape, and other features form unique feature candidate sets, which constitute a high-dimensional feature space and lead to the \"curse of dimensionality\". Manual analysis and attempts to reduce a given feature set to a minimum number of characteristics are subjective, lack a clear scientific methodology, and are impractical [12]. CNNs can automatically extract features, which is a good solution to this problem. Network structure optimization research: CNNs avoid the complex pre-processing of the image through the direct input of original images into the system. In the field of image recognition, CNNs have achieved great success [13]. Research into the structure of convolution neural networks follows two primary trends: 1) increases in the depth of the studied CNN and 2) expansion of the breadth of the CNN. In the research of depth, the potential risks (e.g., network degradation) which are brought about by the deepening of the network are mainly studied to solve the training problems of deep networks (e.g., VGG [14], ResNet [15]). In the research of breadth, the inception of GoogLeNet can be used as a representative (e.g., Inception-v1 [16], Inception-v2 [17] and Inception-v3 [18]). The integration of depth and width has become a new development trend such as with Inception-v4 [18] and Xception [19]. However, these very deep models require high computation complexity and a large number of samples; for example, VGG employed approximately 180 million parameters and GoogLeNet employed 5 million parameters. Furthermore, frequently used public image datasets are massive. These include the CIFAR-10 dataset [20], which consists of 60,000 images in 10 classes; ImageNet [21], which consists of 3.2 million images and 5247 synsets in total; and the COCO dataset [22], which consists of 328,000 images in 91 common object categories. Thus, it is particularly necessary to simplify CNN structure without a public data set containing huge numbers of samples. CNNs in the application of remote sensing classification: CNNs can be applied to the classification of remote sensing images. Many scholars have researched alternative methods in this field. [PERSON] et al. proposed a CNN structure and remote sensing pixel-based image classification was carried out on three hyperspectral remote sensing data sets. The band numbers of the three data sets were 220, 220, and 103, and the sample sizes were 8504, 54,129, and 42,776, respectively [3]. [PERSON] et al. extracted image features from 46,697 hyperspectral remote sensing images containing 103 bands using a CNN approach from a pixel matrix with spectral and spatial features, and classified the features via logical regression [4]. [PERSON] and [PERSON] used two convolution kernel templates of different sizes to extract a variety of remote sensing image features from 8,504 samples containing 220 bands and removed the fully-connected layer for classification purposes [5]. All of the above CNNs required numerous, manually labelled samples; as a result, personal experience likely significantly impacted the classification accuracy; furthermore, the dimensionality of the original remote sensing images was high and included considerable redundant information, which affects the efficiency of network training and impedes network learning.