# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 2 Materials and Methods
### Multiscale Pooling Attention Module  
For DL networks, capturing long-range correlations is crucial. However, the convolution operation is used to process local areas, and its receptive field is limited, so it cannot capture the correlation of long-distance UGS feature information. The pooling operation of the large square kernel can enhance the sharing of global information. This method works well when detecting large-scale objects, but it does not work well when detecting scattered small-scale green spaces. This is because the large kernel extracts too much information from irrelevant regions to adapt to multi-scale changing objects, which can interfere with the final prediction of the model. Therefore, the model needs to have convolutional kernels of multiple sizes to obtain a multi-scale detection field of view to accommodate scale variations of UGS objects.  
PyConv [28] can not only expand the receptive field of the input to capture sufficient UGS contextual information by convolving 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7, etc., but also process inputs of incremental kernel sizes in parallel by using a pyramid structure. This helps the model understand and represent UGS in images of different scales, and to obtain detailed multi-scale information on UGS objects. Global attention mechanism (GAM) [29] preserves channel (Figure 4) and spatial (Figure 5) aspects of information to enhance the importance of cross-dimensional interactions, improving the perception performance of the deep neural network on UGS objects by reducing object information dispersion. Inspired by PyConv and GAM, the MSPA module is built to further enhance object information extraction. As depicted in Figure 1, the MSPA module comes into play after the initial feature extraction phase using ResNet50. Its primary objective is to extract multi-scale contextual information and depth channel-specific information. The MSPA module's capabilities are twofold. First, it leverages contextual information to address objects with similar shapes, such as low vegetation and tree, facilitating their classification. Moreover, it excels in processing and segmenting the intricate edge details between objects. This contextual understanding enhances the model's ability to distinguish and categorize objects effectively. Secondly, the MSPA module extracts multi-scale spatial and channel information concurrently. This parallel extraction approach ensures that the model pays significant attention to both object category information and object location details. This focus on multi-scale aspects allows the model to emphasize essential information within the image, leading to a substantial improvement in segmentation accuracy. In general, the MSPA module, inspired by the principles of PyConv and the Global Attention Mechanism, plays a pivotal role in enhancing object information extraction. It effectively captures multi-scale context and depth channel-specific information, ultimately bolstering the segmentation quality and precision of the model.  
Figure 4: Channel attention submodule.