An effective dual encoder network with a feature attention large kernel for building extraction
Transformer models boost building extraction accuracy by capturing global features from images. However, convolutional networks’ potential in local feature extraction remains underutilized in CNN + Transformer models, limiting performance. To harness convolutional networks for local feature extracti...
Saved in:
| Main Authors: | Shaobo Qiu, Jingchun Zhou, Yuan Liu, Xiangrui Meng |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-01-01
|
| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2375572 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
CDUNeXt: efficient ossification segmentation with large kernel and dual cross gate attention
by: Hailiang Xia, et al.
Published: (2024-12-01) -
Weighted Feature Fusion Network Based on Large Kernel Convolution and Transformer for Multi-Modal Remote Sensing Image Segmentation
by: Jianxia Wang, et al.
Published: (2025-01-01) -
DE-Net: A Dual-Encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point Clouds
by: Zhipeng He, et al.
Published: (2024-01-01) -
Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism
by: Hao Yan, et al.
Published: (2024-12-01) -
BuildNext-Net: A Network Based on Self-Attention and Equipped With an Efficient Decoder for Extracting Buildings From High-Resolution Remote Sensing Images
by: Changsheng OuYang, et al.
Published: (2025-01-01)