Transformer 特征引导的双阶段地图智能生成

Objective Map intelligent generation technique is focused on generating map images quickly and cost efficiently. For existing intelligent map generation technique，to get quick-responsed and low-cost map generation，remote sensing image is taken as the input，and its generative adversarial network（GAN）is used to generate the corresponding map image. Inevitably，it is challenged that the intra-class differences within geographical elements in remote sensing images and the differences of geographical elements between domains in the map generation task are still not involved in. The intra-class difference of geographical elements refers that similar geographical elements in remote sensing images have several of appearances，which are difficult to be interpreted. Geographical elements segmentation is required for map generation in relevance to melting obvious intra-class differences into corresponding categories. The difference of geographical elements between different domains means that the corresponding geographical elements in remote sensing images and map images are not exactly matched well. For example，the edges of vegetation elements in remote sensing images are irregular，while the edges of vegetation elements in map images are flat. Another challenge for map generation is to generate and keep consistency to the features of map elements. Aiming at the intra-class difference of geographical elements and the superposition of geographical elements，we develop a dual of map-intelligent generation method based on Transformer features. Method The model consists of three sorts of modules relevant to feature extraction，preliminary and refined generative adversarial contexts. First，feature extraction module is developed based on the latest Transformer network. It consists of a backbone and segmentation branch in terms of Swin-Transformer structure. Self-attention mechanism based Transformer can be used to construct the global relationship of the image，and it has a larger receptive field and it can extract feature information effectively. The segmentation branch is composed of a pyramid pooling module（PPM）and a feature pyramid network（FPN）. To get more effective geographic element features，feature pyramid is employed to extract multi-level feature information，and the high-level geographic element semantic information can be integrated into the middle-level and low-level geographic element semantic information，and the PPM is used to introduce the global semantic information as well. Next，feature information is sent to the segmentation branch，which uses the actual segmentation results as a guidance to generate effective geographical element features. To guide map generation and resolve the problem of map generation caused by the differences in geographical elements，this module can be used to extract the features of geographical elements in remote sensing images. Third，the preliminary generative adversarial module has a preliminary generator and a discriminator. The preliminary generator is a multi-scale generator，consisting of a local generator and a global generator，and it is used to generate the high-resolution images. Both of local and global generators are linked to encoder/decoder structures. The input of the preliminary generator is derived of remote sensing image and geographical element features，and the output is originated from preliminary map image. The discriminator is also recognized as a multi-scale discriminator，which consists of three sorts of sub discriminators for the high-resolution images. The input of the discriminator is the generated map and the real map，and the output is the single channel confidence map. Finally，a refined generator is used for refined generative adversarial module，and a discriminator with the preliminary generative adversarial module is shared in as well. The structure of the refined generator is same as the preliminary generator，which is also as a multi-scale generator in terms of local and global generators. The input of the refinement generator is originated from a preliminary map image and the output is derived of a fine map image. A dual of generation framework is constructed in terms of refined and preliminary generative adversarial-related modules. In general，to obtain preliminary map images，the preliminary generative adversarial module is as inputs based on remote sensing images and geographical element features. The preliminary map image is rough，and there are incomplete geographical elements，such as uneven road edges and fractures. For the refined generative adversarial module，to learn the geometric characteristics of geographical elements in the real map，obtain high-quality fine map images，and alleviate the problem of inaccurate local map generation caused by the differences of geographical elements between domains，the generated primary map image is taken as the input，and the real map is taken as the guide as well. Result Experiments are carried out on 9 regions on the aerial image dataset for online map generation （AIDOMG）dataset in comparison with 10 sort of popular methods. For the Haikou area，Frechet inception distance（FID）is reduced by 16. 0%，Wasserstein distance（WD）is reduced by 4. 2%，and the 1-nearest neighbor（1-NN）is reduced by 5. 9% as well. For the Paris area，FID is decreased by 2. 9%，WD is decreased by 1. 0%，and 1-NN decreased by 2. 1% simultaneously. Comparative analyses demonstrate that our method proposed can improve the results of map generation effectively. At the same time，ablation studies of the model can show the effectiveness of each module，and each module can be added and the model results is improved gradually as well. Conclusion To solve the problem of poor map generation quality caused by the intra-class inconsistency of geographical elements effectively，a dual of Transformer features-related map-intelligent generation method is proposed，and the differences of geographical elements between domains can be illustrated via high-quality Transformer-guided feature and a dual of generation framework further.