)1 College of Astronomy, Henan Agricultural University, Zhengzhou 4500046, China; ruoyangli, [EMAIL_ADDRESS] (R.L. )1 College of Astronomy, Henan Agricultural University, Zhengzhou 4500046, China; ruoyangli, [EMAIL_ADDRESS] (R.L. )1 College of Astronomy, Henan Agricultural University, Zhengzhou 4500046, China; ruoyangli, [EMAIL_ADDRESS] (R.L. )1 College of Astronomy, Henan Agricultural University, Zhengzhou 4500046, China; ruoyangli, [EMAIL_ADDRESS] (R.L. )1 College of Astronomy, Henan Agricultural University, Zhengzhou 4500046, China; ruoyangli, [EMAIL_ADDRESS] (R.L. )1 College of Astronomy, Henan Agricultural University, Zhengzhou 4500046, China; ruoyangli, [EMAIL_ADDRESS] (R.L. )1 College of Astronomy, Henan Agricultural University, Zhengzhou 4500046, China; ruoyangli, [EMAIL_ADDRESS] (R.L.L. )1 College of Astronomy, Henan Agricultural University, Zhengzhou 4500046, China; ruoyangli, hau@163.  
dependencies. Most studies have predominantly utilized multi-scale contexts formed by pixels that are spatially adjacent or sampled. For instance, the Pyramid Pooling Module (PPM) in PSPNet [3] segregates all pixels into several regions, selecting all pixels within the same region for context. In the architecture of DeepLabv3 [4], the Atrous Spatial Pyramid Pooling (ASPP) module uses convolutional kernels of various sizes for image processing. This approach enables the capture of both minute details and the overall structure within images. In the Transformer [5] model, self-attention and multi-head attention mechanisms aid the model in focusing on crucial areas of RS images, allowing it to concurrently pay attention to several significant sections of the image and comprehend their interrelations. Therefore, the pixels chosen by the PPM context, ASPP context, and the context from the self-attention and multi-head attention mechanisms often constitute a mix of object pixels, relevant background pixels, and irrelevant background pixels. Nonetheless, such models typically exhibit high computational complexity and slow inference times, rendering them challenging for deployment on various embedded platforms. Currently, research in RS segmentation has evolved from focusing on accuracy improvements to optimizing for speed. MobileNetV2 [6] deconstructs standard convolutions into group convolutions and pointwise convolutions. It groups different feature maps of the input layer, then applies different convolution kernels to each group, thereby reducing the computational load of the convolutions. Based on this, depthwise separable convolution modules and inverted residual modules were developed, achieving significant compression of the network parameters. Moreover, the parameters of MobileNetV3 [7] were obtained through Network Architecture Search (NAS), and the Squeeze-and-Excitation (SE) channel attention mechanism was introduced [8], effectively enhancing network performance further. However, the grouping operation in MobileNet, due to the lack of connections between different groups, results in very limited learned features and can easily lead to the loss of semantic information. Therefore, researchers proposed ShuffleNet [9]. Its core design principle is to shuffle different channels to address the issue of homogeneity brought by group convolution, using multiple convolutional layers to construct a more robust structure. PeleeNet [10], inspired by MobileNetV1 [11], is a lightweight network architecture that utilizes Two-Way Dense Layers to enhance the flow of features both forward and backward, capturing a broader array of effective features. However, when these lightweight structures are applied to RS image segmentation, they often overlook the redundant features in RS images, significantly reducing the precision of feature capture. Moreover, relying solely on the semantic information of individual pixels is often insufficient for accurately determining their category, leading to a noticeable decline in the model's segmentation performance.