In this research, we present the Spatial-Aware Transformer (SAT), an enhanced implementation of the Swin Transformer module, purposed to augment the global modeling capabilities of existing transformer segmentation mechanisms within remote sensing. The current landscape of transformer segmentation techniques is encumbered by an inability to effectively model global dependencies, a deficiency that is especially pronounced in the context of occluded objects. Our innovative solution embeds spatial information into the Swin Transformer block, facilitating the creation of pixel-level correlations, and thereby significantly elevating the feature representation potency for occluded subjects. We have incorporated a boundary-aware module into our decoder to mitigate the commonly encountered shortcoming of inaccurate boundary segmentation. This component serves as an innovative refinement instrument, fortifying the precision of boundary demarcation. After these strategic enhancements, the Spatial-Aware Transformer achieved state-of-the-art performance benchmarks on the Potsdam, Vaihingen, and Aerial datasets, demonstrating its superior capabilities in recognizing occluded objects and distinguishing unique features, even under challenging conditions. This investigation constitutes a significant advancement toward optimizing transformer segmentation algorithms in remote sensing, opening a wealth of opportunities for future research and development.

The purpose of semi-supervised video object segmentation (VOS) is to predict and generate object masks in subsequent video frames after being provided with the initial frame’s object mask. Currently, mainstream methods leverage historical frame information for enhancing the network’s performance. However, this approach faces the following issues: (1) They often overlook important shape information, leading to decreased accuracy in segmenting object-edge areas. (2) They often use pixel-level motion estimation to guide the matching for addressing distractor objects. However, this brings heavy computation costs and struggle against occlusion or fast/blurry motion. For the first problem, this paper introduces an object shape extraction module that exploits both the high-level and low-level features to obtain object shape information, by which the shape information can be used to further refine the predicted masks. For the second problem, this paper introduces a novel object-level motion prediction module, in which it stores the representative motion features during the training stage, and predicts the object motion by retrieving them during the inference stage. We evaluate our method on benchmark datasets compared with recent state-of-the-art methods, and the results demonstrate the effectiveness of the proposed method.