# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 1 Introduction  
Remote sensing is a detection technology that obtains information about objects without direct contact at long distances. It plays a very important role in many studies, and an increasing number of more precise sensors and measurements are also providing researchers with more information [1]. It has a growing impact in a wide variety of areas from business to science to public policy. Synthetic Aperture Radar (SAR) is one of the active remote sensing technologies and has a unique ability to obtain high-resolution microwave images of the Earth's surface targets in almost all weather conditions. Automatic Target Recognition (ATR) based on the SAR images, especially ATR through deep learning, has played an essential role in wide-area monitoring of targets such as vehicles, ships, and aircraft [2; 3; 4]. The commonly used training method for the target recognition model is the one-time supervised learning or batch learning mode to process the data in an offline manner [5; 6], as shown in Figure 0(a). All existing labeled target samples are trained to predict the labels of unknown input data. However, it is usually challenging to collect the labeled samples of all the target classes at once in practice, and the training data of the new target classes are obtained gradually. The storage requirement of training samples increases as well. The trained target recognition model using old target samples needs to be retrained using bothold and new target samples. Due to the limitation of computation and storage resources in some particular application situations, this approach cannot be applied. The incremental learning (also known as lifelong learning, continuous learning) emerges to enable trained target recognition model to continuously learn new tasks [7]. It keeps knowledge gained from previous tasks without the need to preserve large amounts of old data [8], as shown in Figure 1b. Incremental learning focuses the problem of catastrophic forgetting [9; 10; 11]. The catastrophic forgetting means that after a target recognition model is trained with a new dataset, the weights necessary for the old tasks are changed to adapt to the new task, and the knowledge of the previously learned task is lost. Although some incremental learning methods have been proposed, there are still three problems needed to be addressed. (1) The performance of the incremental process still degrades significantly for old target classes, i.e., incremental learning faces the problem of catastrophic forgetting, and there are varying degrees of performance degradation as the classes increase. Regarding the problem of catastrophic forgetting, some methods are proposed, such as preserving a small number of training samples of old classes [8; 12], using a sub-network for each incremental phase [13; 14], freezing the nodes of the network essential for the old task during the training of the model [15; 16]. Among them, the old sample preservation is a straightforward way to prevent the model from forgetting what it has learned. However, it is difficult to ensure that the preserved old samples are representative enough to train a new model with good recognition performance. Although the methods in [17; 18] learn to generate pseudo-samples of the old data to reconstruct the data distribution of the old classes, the quality of the pseudo-samples is not able to entirely represent the actual samples. It is worth noting that the knowledge distillation [19], which is initially proposed for transfer learning, is very effective in enabling the current model to simulate the old model. Therefore, it can be introduced into incremental learning. However, the standard knowledge distillation method builds relevant models by minimizing the Kullback-Leibler Divergence (KLD) between probabilistic outputs, and ignores important knowledge structures in the old model [20]. In this paper, considering the advantage of knowledge distillation and old  
Figure 1: Illustration of the non-incremental learning and incremental learning process.