# Enhanced Atrous Extractor and Self-Dynamic Gate Network for Superpixel Segmentation
## 1 Introduction  
A superpixel is a group of pixels with similar color, texture, and low-level and mid-level properties. Superpixel segmentation aims to divide the image with several superpixels, which can reduce the basic primitive effectively. Therefore, the computation cost could be reduced vastly. Recently, the superpixel segmentation algorithm has been applied in remote sensing fields, which is used to reduce the dimension of features to speed up the training and inference time. Therefore, superpixel segmentation can open up some new scenarios in remote sensing. An example of this is ESCNet [1], which introduces a superpixel to reduce the latent noise of the pixel-level feature maps while preserving the edges. SG-waterNet [2] introduces superpixels to produce a superpixel graph, which contains more powerful context information that can be exploited by the GCN. The MAST [3] model takes advantage of the adaptive spatial nature of superpixels to achieve better classification performance with high-resolution remotely sensed images. With these applications of superpixel segmentation algorithms [4; 5; 6; 7; 8; 9; 10], superpixel segmentation has become a key technology in the remote sensing of the computer vision field. However, all these applications of superpixel methods introduce the traditional k-means superpixel segmentation algorithm [11; 12; 13; 14], which still suffers from the hand-craft feature and is non-differentiable. An example of this is SLIC [15], which first initializes the seed and computes the associate map between the seeds and the surrounding pixels. SNIC [16] introduces a priority queue to assign the pixels to the correct seeds. LSC [17] introduces mapping the property to the high-dimension space to obtain the superpixels. Allthese traditional k-means superpixel segmentation algorithms are difficult to incorporate into the convolutional neural network and can not get the accuracy of the superpixel map. In order to fix the problems, some deep-learning-based methods are proposed to fix the superpixel segmentation. An example of this is SSN [18], which computes a differentiable soft associate map between pixels and seeds. SCN [19] first proposes an end-to-end superpixel segmentation network. However, all these deep-learning-based methods still suffer from under-segmentation and low compactness in remote sensing images. To solve these problems, we propose EAGNet, the enhanced atrous extractor and self-dynamic gate network. The enhanced atrous extractor introduces our proposed enhanced atrous convolution and transformer architecture based on a multi-scale pixel feature to extract a multi-scale superpixel feature with contextual information. In particular, the enhanced atrous extractor first introduces the atrous convolution with the SiLU function to extract the multi-scale superpixel feature and feed it into the MLP. The self-dynamic gate network introduces the gating and dynamic mechanism to inject the pixel information. Specially, the self-dynamic gate network introduces the convolution and sigmoid to produce the gate of the pixel and the superpixel feature by themselves. The multi-scale superpixel feature with contextual information is useful for solving the low compactness problem. Our self-dynamic gate can solve the under-segmentation of remote sensing images. We conduct massive experiments on the BSDS500 dataset [20] and UCM dataset [21] to show that our EAGNet can not only fix the under-segmentation and low compactness of remote sensing images but can also achieve state-of-the-art performance among traditional k-means superpixel segmentation and deep-learning-based algorithms. We also conduct numerous ablation studies to prove the effectiveness of our proposed method. Our main contributions can be listed as follows:  
1. We propose an enhanced atrous extractor, which introduces enhanced atrous convolution based on a transformer architecture to extract multi-scale superpixel features with contextual information. 2.