Elyar Adil
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Elyar Adil has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jianxi Yang
School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China
1 shared publication - Pingping Huang
College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China
1 shared publication - Weixian Tan
College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China
1 shared publication - Xiangli Yang
School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, China
1 shared publication - Xiaolong Liu
College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
1 shared publication
Latest Publications
- Open Access
- 15 December 2022
- Published by MDPI AG in Remote Sensing
- Vol. 14 (24) , 6361
Cascaded U-Net with Training Wheel Attention Module for Change Detection in Satellite Images
Change detection is an important application of remote sensing image interpretation, which identifies changed areas of interest from a pair of bi-temporal remote sensing images. Various deep-learning-based approaches have demonstrated promising results and most of these models used an encoder–decoder shape such as U-Net for segmentation of changed areas. In order to obtain more refined features, this paper introduces a change detection model with cascaded U-Net. The proposed network architecture contains four cascaded U-Nets with ConvNeXT blocks. With a patch embedding layer, the cascaded structure can improve detection results with acceptable computational overhead. To facilitate the training of the cascaded N-Nets, we proposed a novel attention mechanism called the Training whEel Attention Module (TEAM). During the training phase, TEAM aggregates outputs from different stages of cascaded structures and shifts attention from outputs from shallow stages to outputs from deeper stages. The experimental results show that our cascaded U-Net architecture with TEAM achieves state-of-the-art performance in two change detection datasets without extra training data.
- 15 December 2022
- Published by MDPI AG in Remote Sensing
- Vol. 14 (24) , 6361
Open AccessCascaded U-Net with Training Wheel Attention Module for Change Detection in Satellite Images
Change detection is an important application of remote sensing image interpretation, which identifies changed areas of interest from a pair of bi-temporal remote sensing images. Various deep-learning-based approaches have demonstrated promising results and most of these models used an encoder–decoder shape such as U-Net for segmentation of changed areas. In order to obtain more refined features, this paper introduces a change detection model with cascaded U-Net. The proposed network architecture contains four cascaded U-Nets with ConvNeXT blocks. With a patch embedding layer, the cascaded structure can improve detection results with acceptable computational overhead. To facilitate the training of the cascaded N-Nets, we proposed a novel attention mechanism called the Training whEel Attention Module (TEAM). During the training phase, TEAM aggregates outputs from different stages of cascaded structures and shifts attention from outputs from shallow stages to outputs from deeper stages. The experimental results show that our cascaded U-Net architecture with TEAM achieves state-of-the-art performance in two change detection datasets without extra training data.