HA-MPPNet: Height Aware-Multi Path Parallel Network for High Spatial Resolution Remote Sensing Image Semantic Seg-Mentation

ISPRS International Journal of Geo-Information

Chaoqun Wu¹  Suting Chen¹  Mithun Mukherjee²  Yujie Zheng³ 

[1] Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China;School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China;The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210008, China;

关键词: remote sensing image;    semantic segmentation;    high spatial resolution;    gated feature fusion;    digital surface model (DSM);    height features;   

DOI  :  10.3390/ijgi10100672

来源: DOAJ

【 摘 要 】

Semantic segmentation of remote sensing images (RSI) plays a significant role in urban management and land cover classification. Due to the richer spatial information in the RSI, existing convolutional neural network (CNN)-based methods cannot segment images accurately and lose some edge information of objects. In addition, recent studies have shown that leveraging additional 3D geometric data with 2D appearance is beneficial to distinguish the pixels’ category. However, most of them require height maps as additional inputs, which severely limits their applications. To alleviate the above issues, we propose a height aware-multi path parallel network (HA-MPPNet). Our proposed MPPNet first obtains multi-level semantic features while maintaining the spatial resolution in each path for preserving detailed image information. Afterward, gated high-low level feature fusion is utilized to complement the lack of low-level semantics. Then, we designed the height feature decode branch to learn the height features under the supervision of digital surface model (DSM) images and used the learned embeddings to improve semantic context by height feature guide propagation. Note that our module does not need a DSM image as additional input after training and is end-to-end. Our method outperformed other state-of-the-art methods for semantic segmentation on publicly available remote sensing image datasets.

【 授权许可】

Unknown