no code implementations • 19 Dec 2022 • Yonghao Xu, Tao Bai, Weikang Yu, Shizhen Chang, Peter M. Atkinson, Pedram Ghamisi
Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field.
1 code implementation • 18 Sep 2021 • Libo Wang, Rui Li, Ce Zhang, Shenghui Fang, Chenxi Duan, Xiaoliang Meng, Peter M. Atkinson
In this paper, we propose a Transformer-based decoder and construct a UNet-like Transformer (UNetFormer) for real-time urban scene segmentation.
Ranked #1 on Scene Segmentation on UAVid
1 code implementation • IEEE Transactions on Geoscience and Remote Sensing 2021 • Rui Li, Shunyi Zheng, Ce Zhang, Chenxi Duan, Jianlin Su, Libo Wang, Peter M. Atkinson
A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention.
Ranked #7 on Semantic Segmentation on ISPRS Vaihingen
no code implementations • 14 Mar 2021 • Libo Wang, Ce Zhang, Rui Li, Chenxi Duan, Xiaoliang Meng, Peter M. Atkinson
However, MSR images suffer from two critical issues: 1) increased scale variation of geo-objects and 2) loss of detailed information at coarse spatial resolutions.
2 code implementations • 1 Feb 2021 • Zak Varty, Jonathan A. Tawn, Peter M. Atkinson, Stijn Bierman
We consider how improvements to data quality over time can be incorporated when selecting a modelling threshold and in the subsequent inference of an extreme value analysis.
Methodology Applications 62G32
1 code implementation • 22 Nov 2020 • Xinzheng Zhang, Hang Su, Ce Zhang, Xiaowei Gu, Xiaoheng Tan, Peter M. Atkinson
In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning.
2 code implementations • 26 Jul 2020 • Rui Li, Chenxi Duan, Shunyi Zheng, Ce Zhang, Peter M. Atkinson
In this Letter, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected and asymmetric-convolution-based U-Net (MACU-Net), for segmentation using fine-resolution remotely sensed images.
no code implementations • 3 Mar 2020 • Xinzheng Zhang, Hang Su, Ce Zhang, Peter M. Atkinson, Xiaoheng Tan, Xiaoping Zeng, Xin Jian
Parallel FCM are utilized on these two mapped DDIs to obtain three types of pseudo-label pixels, namely, changed pixels, unchanged pixels, and intermediate pixels.
no code implementations • 17 Jan 2020 • Xinzheng Zhang, Guo Liu, Ce Zhang, Peter M. Atkinson, Xiaoheng Tan, Xin Jian, Xichuan Zhou, Yongming Li
The prediction of this Phase is the set of changed and unchanged superpixels.
no code implementations • 19 Dec 2018 • Pedram Ghamisi, Behnood Rasti, Naoto Yokoya, Qunming Wang, Bernhard Hofle, Lorenzo Bruzzone, Francesca Bovolo, Mingmin Chi, Katharina Anders, Richard Gloaguen, Peter M. Atkinson, Jon Atli Benediktsson
The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data.