# A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
## 1 Introduction  
Modern remote sensing satellites possess unprecedented high-frequency access capabilities, thereby obtaining multi-temporal remote sensing imagery that encapsulates vast amounts of information about features on the Earth's surface in both space and time [1]. Fully harnessing this spatio-temporal information will further enhance the applications of remote sensing imagery in environmental monitoring, urban planning, and agricultural land classification. Food security serves as a crucial foundation for national security, and arable land stands as the lifeline of food production. As a result, governments worldwide have consistently placed great emphasis on the supervision of arable land protection and have implemented strict measures to ensure the sustainable utilization of arable land resources [2]. However, the \"non-graification\" [3] phenomenon in arable land, resulting from structural adjustments in land use within agricultural areas, is challenging to capture in single-temporal remote sensing images. This limitation often leads to unsatisfactory outcomes in arable land segmentation. To better monitor arable land protection and effectively manage arable land resources, it is necessary to employ multi-temporal remote sensing imageryfor comprehensive analysis [4; 5]. This paper examines satellite image time series (SITS) to acquire information about crop growth, enabling the long-term monitoring of arable land and elevating the level of agricultural intelligence. In recent years, the use of deep learning methods for semantic segmentation of remote sensing images has become the mainstream approach for land cover classification. Researchers in this field have conducted in-depth investigations on single-temporal remote sensing images and have achieved remarkable results [6; 7]. [PERSON] et al. [8] integrated the Atrous Spatial Pyramid Pooling (ASPP) module, which encodes image-level features, into the U-Net [9] network, significantly improving the segmentation accuracy of multi-scale features in the imagery. However, mainstream segmentation networks are unable to fully recover spatial information discarded in the feature extraction stage, which exacerbates the segmentation inaccuracies caused by fuzzy land feature boundaries in the imagery. To address this, [PERSON] et al. [10] introduced the HRNet network, which better preserves spatial information during feature extraction, enhancing the accuracy and precision of segmentation, while also considering global context and multi-scale features. Compared to natural images, lower-resolution remote sensing imagery relies more on contextual information for pixel-level classification. [PERSON] et al. [11] introduced the parcel attention module to enhance the extraction of contextual information through local attention, and the attention embedding module to fuse semantic information at different levels. The remote sensing field has continuously been seeking new methods to enhance the accuracy and efficiency of remote sensing image segmentation. With the tremendous success of Transformer models in the field of computer vision, applying them to remote sensing image segmentation has become a trend. By studying the performance of remote sensing images within Transformer models, we can gain a deeper understanding of how Transformer models work and their characteristics, further promoting their application and development in the remote sensing domain [12]. To fully leverage the capabilities of convolutional neural networks (CNNs) in local feature extraction and the advantages of the Transformer [13] in capturing global contextual information, the fusion of both is a viable choice. [PERSON] et al. [14] proposed the CCTNet, which combines the local detail features from the CNN and the global contextual information from the Transformer, effectively mitigating the misclassifications of small objects and gaps in the imagery. [PERSON] et al.