A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery

Chen, Jinzhi; Zhang, Dejun; Wu, Yonghong; Chen, Yilin; Yan, Xiaohu

doi:10.3390/rs14092276

Cited by 26 publications

(14 citation statements)

References 50 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the WHU dataset, comparison experiments are conducted with Unet [47], DeepLabv3+ [48], PSPNet [49], Map-Net [50], BOMSC-net [28], DR-Net [51], MBR-HRNet [52], and CFENet [53] In the given roof material transformation case in (a), Unet and PSPnet exhibit considerable instances of false red classification, while MDBES-Net avoids semantically segregating metal shelves into buildings. For Row (b) medium to large buildings, BOMSC-net reveals more instances of missed blue areas for extended large buildings, while MDBES-Net provides a more comprehensive extraction for larger buildings.…”

Section: ) Analysis Of Experimental Resultsmentioning

confidence: 99%

MDBES-Net: Building Extraction From Remote Sensing Images Based on Multiscale Decoupled Body and Edge Supervision Network

Xu,

Du,

Meng

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The extraction of buildings in aerial remote sensing applications is an important and challenging task. Most existing methods extract buildings based on local area attention, ignoring the loss of accuracy due to the global structure of the building. However, global structural features of buildings with strong coupling relationships in complex scenes are difficult to extract, such as the edges and bodies of buildings, leading to discontinuous results. Therefore, Multiscale Decoupled Body and Edge Supervision Network (MDBES-Net), which can consider both edge optimization and inner consistency, is proposed to solve these problems. MDBES-Net consists of the Body-Mask-Edge Consistency Constraint base network (BMECC), Decoupling the Body and Edge Aware module (DBEA), and the Channel Decoupled Attention module (CDA). First, Body-Mask-Edge consistency constraint supervision is established by body and edge labels to jointly improve the segmentation effect in the BMECC base network. Second, In the mutiscale DBEA module, building features are warped by a learnable flow field to make body parts more consistent and edges more detailed. Finally, the CDA module performs adaptive calibration of the re-coupled feature map channel response to minimize external background noise interference. Experiments on the open Massachusetts Building Dataset, WHU Building Dataset show that the proposed MDBES-Net can accurately extract buildings in complex scenarios, enabling complete building segmentation with refined boundaries and improved internal consistency.

show abstract

Section: ) Analysis Of Experimental Resultsmentioning

confidence: 99%

MDBES-Net: Building Extraction From Remote Sensing Images Based on Multiscale Decoupled Body and Edge Supervision Network

Xu,

Du,

Meng

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…These methods are DR-Net, 30 ASLNet, 31 MAP-Net, 9 MultiBuild-Net, ? BOMSC-Net, 33 CFENet, 34 and MECNet. 18 The qualitative results are reported in Table 3 and Table 4.…”

Section: Comparison With Building Footprint Extraction Methodsmentioning

confidence: 99%

An edge-guided network for building footprint extraction from optical remote sensing images

Zhou,

Tan

2024

MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

View full text Add to dashboard Cite

Accurate building footprint extraction from optical remote sensing images remains challenging due to the diverse appearance and complex scenarios. Although recent deep learning-based methods have been shown to greatly improve the accuracy of building footprint extraction, vanilla deep networks still suffer from ambiguous predictions of edge pixels. The building edge contains abundant location and shape information, which is important for downstream applications such as building positioning and area measurement. Therefore, the problem of inaccurate edge prediction needs to be resolved urgently. To this end, we propose a novel edge-guided network (EGNet) that makes ample use of the edge prior in an end-to-end manner. First, an edge extraction module (EEM) is proposed to extract the building edge map. Then, an edge-guidance module (EGM) is designed to utilize the edge map to guide each encoder block in extracting edge-related features. Furthermore, a multi-scale context aggregation module (MCAM) is built to enhance the feature representation by aggregating contextual semantics with different receptive fields. EGNet can effectively mine edge semantics and guide the representation learning of boundaries, achieving 75.21% and 91.16% IoU on the Massachusetts and WHU datasets, respectively. Experimental results demonstrate that the proposed EGNet has a certain superiority in both accuracy and efficiency compared with current state-of-the-art (SOTA) methods.

show abstract

“…We used the same settings in the InriaAIL dataset. We also cite recent work to validate the effectiveness of our method, namely CFENet [44]. Due to the limitation of GPU memory, we chose SST (RS18, S4) and HRNet-w30.…”

Section: A Experiments On Whu Building Datasetmentioning

confidence: 99%

MSB-Net: An End-to-End Network for Extracting Building from High-Resolution Remote Sensing Imagery

Lan,

Wei,

Huang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Extracting buildings from high-resolution remote sensing imagery (HRSI) is of great significance to emergency management, land resource utilization and analysis, as well as city planning and construction. However, due to the complex backgrounds and diverse appearances and different sizes of buildings in HRSI, most existing methods for automatic building extraction are difficult to obtain strong building feature representation from low-level and high-level features. Furthermore, existing research mainly focused on regional accuracy, while less attention was paid to the description of building boundaries. In this paper, MSB-Net, an end-to-end neural network, is proposed to address these issues. A multi-scale feature fusion module (MSFFM) is designed to capture and fuse multi-scale features. A local branch (LB) constructed by the MSFFM and position attention, is used to obtain long range of context information between different positions and extract the essential features of buildings (e.g., shapes, edges) from low-level features. And a global branch (GB) is designed to use the MSFFM and channel attention to enhance high-level features. Therefore, our method can not only obtain information on building-related attribute categories, but also capture the rich context information in channel dimensions. The boundary enhancement and completion module (BECM) take the output of the GB and LB as input to search for the missing parts and details of buildings to improve the segmentation accuracy and boundary quality. Our method is tested on two public building datasets and achieves superior classification performance. Index Terms-multi-scale feature fusion, boundary enhancement, building extraction I. INTRODUCTIONxtracting buildings from high-resolution remote sensing imagery (HRSI) can be of great help in various fields, including emergency management, land resource utilization, city planning and construction [1],[2], [3] and so on. With the improvement of HRSI data quality Th is paragraph of the first footnote will contain the date on which you submitted your paper for review, which is populated by IEEE. It is IEEE style to display support information, including sponsor and financial support acknowledgment, here and not in an acknowledgment section at the end of the article. For example, "This work was supported in part by the U.S. Department of Commerce under Grant 123456." The name of the corresponding author appears after the financial information, e.g.

show abstract

A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery

Cited by 26 publications

References 50 publications

MDBES-Net: Building Extraction From Remote Sensing Images Based on Multiscale Decoupled Body and Edge Supervision Network

MDBES-Net: Building Extraction From Remote Sensing Images Based on Multiscale Decoupled Body and Edge Supervision Network

An edge-guided network for building footprint extraction from optical remote sensing images

MSB-Net: An End-to-End Network for Extracting Building from High-Resolution Remote Sensing Imagery

Contact Info

Product

Resources

About