# A Deep Curriculum Learning Semi-Supervised Framework for Remote Sensing Scene Classification
## 1 Introduction  
With the rapid development of space science and technology, sensor technology, and aerial remote sensing information acquisition technology, the ability to acquire remote sensing images is becoming stronger and stronger, and the resolution of these images is becoming higher and higher. Therefore, high-resolution remote sensing (HRRS) image classification has become an important research topic in the field of remote sensing. The results of HRRS scene classification can be used in many practical applications, such as environmental monitoring, precision agriculture, urban planning, etc. [1, 2, 3, 4, 5]. The available labeled images are usually inadequate for training a supervised deep learning model, which must deal with the dramatic growth in HRRS images in the field of remote sensing. Furthermore, labeling the observed data is time- and cost-intensive. Consequently, it is desirable to utilize a large amount of unlabeled data to enhance the classification capability of deep learning networks when using a limited amount of labeled data. To address this problem, a semi-supervised image classification method is proposed, which can express the information revealed using a limited number of labeled images and a sufficient number of unlabeled images. [PERSON] et al. [6] proposed learning from unlabeled examples using an end-to-end prototypical network for remote sensing image classification. [PERSON] et al. [7] assumed that difficult images have different levels of difficulty and reorganized unlabeled images in order from easy to difficult, making use of curriculum learning (CL) to perform semi-supervised image classification. A CL framework based on graph label propagation was proposed, which is designed in two stages, named teaching-to-learn and learning-to-teach (TLLT). However, the disadvantage is that the time and cost of building a graph are high, especially when the number of training samples and the number of feature dimensions are large. Therefore, CL generally uses traditional features with low dimensions, but its expression ability is limited; meanwhile, deep learning has very strong feature representation capabilities, but its dimensions are high. To this end, we propose CL based on deep feature fusion to create a semi-supervised HRRS image classification framework. The framework is divided into three parts: multimodal deep feature extraction, deep feature fusion, and multimodal curriculum learning. This framework can effectively utilize the rich high-level semantic features of deep learning; after deep feature fusion, the deep features have stronger discriminative power and low-dimensional features; thus, they can reduce the training cost of semi-supervised CL. The contributions of this paper are as follows:  
1. A semi-supervised CL method based on deep learning feature fusion is proposed. This method can make good use of different deep learning networks to extract rich semantic features, perform feature fusion, and make use of sufficient information to achieve the purpose of removing feature redundancy and reducing the dimensions. A method employing pretrained networks not only can save training time but can also extract rich semantic features. 2. Semi-supervised classification adopts the method of multimodal CL, which can progress well and reorganize and learn using easy-difficult samples. Thus, the consistency and complementarity of various deep features can be fully realized. 3. The DCA method is introduced to compress and fuse the deep features. It not only retains effective information for the discrimination of multiple deep features participating in the fusion but also eliminates the redundant information between feature vectors, greatly improving the efficiency of semi-supervised learning. The rest of this paper is organized as follows. Section 2 describes the details of semi-supervised learning, feature fusion, and the curriculum learning methodology. The CL framework is then discussed in Section 3.