Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

By Yongyang Xu, Liang Wu, Zhong Xie, Zhanlong Chen. Remote Sensing, 2018

442 citations recorded by the source corpus.

Very high resolution (VHR) remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.

Subfields: Ecology, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/5ff64afd70434b12e043ff39a91271eab6391124

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Wanli Ouyang
2. Lei Bai
3. Sijie Zhao
4. Hao Chen
5. Yanfei Zhong
6. Pengfeng Xiao
7. Xue-liang Zhang

In Media Technology

1. Zhuo Zheng
2. Wanli Ouyang
3. Lei Bai
4. Sijie Zhao
5. Hao Chen
6. Yanfei Zhong
7. Pengfeng Xiao
8. Junjue Wang

Citations in the corpus

• Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery (2020, methodology): “Semantic Segmentation in Remote Sensing Community There are a lot of applications using semantic segmentation technique in the remote sensing community, such as land use and land cover (LULC) classification [46, 23, 47], building extraction [45, 24, 43, 15], road extraction [28, 9, 2, 34, 3], vehicl”
• RS-Mamba for Large Remote Sensing Image Dense Prediction (2024, background): “The images are divided into 4736 images designated for training, 1036 for validation, and 2416 for testing purposes, each with a spatial resolution of 0.3 m.”