.png){kind=icon}
返回
A Collaborative Correlation-Matching Network for Multimodality Remote Sensing Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2022.3170312.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, with the increasing availability of the high-quality panchromatic (PAN) and multispectral (MS) remote sensing (RS) images, the inherent complementarity between PAN and MS images provides a wide development prospect for the multimodality RS image classification task. However, how to cleverly relieve the modal differences and effectively integrate the single-modality PAN and MS features is still a challenge. In this article, we design a collaborative correlation-matching network (CCM-Net) for multimodality RS image classification. Concretely, we first propose a bidirectional dominant feature supervision (Bi-DFS) learning, it utilizes single-modality dominant features as supplementary supervision information to establish the joint optimization loss function, thereby adaptively narrowing the differences between modalities before the feature extraction. In the feature extraction stage, the interactive correlation feature matching (ICFM) learning, composing the spatial feature matching (Spa-FM) and spectral feature matching (Spe-FM) strategies, is proposed to establish interactive matching and enhancement between multimodality strong correlation features from the perspective of spatial and spectral, respectively, thereby effectively alleviating the semantic deviation of multimodality features. Finally, we aggregate finer multilevel multimodality features to obtain top-level features with high discrimination. The effectiveness of the proposed algorithm has been verified on multiple datasets. Our code is available at: https://github.com/Momuli/CCM-Net.git.
Keyword:
Feature extraction
Remote sensing
Correlation
Semantics
Spatial resolution
Task analysis
Image color analysis
Correlation-matching
deep learning
multimodality classification
panchromatic (PAN) and multispectral (MS) image
remote sensing (RS)
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
Caw’s Walking State Recognition Based on Accelerometers and Gyroscopes Installed on Ear-Tags and Collar-Tags基于安装在耳标和项圈上的加速度计和陀螺仪的Caw步行状态识别
PREAI
分享收藏RGB-D Scene Recognition via Spatial-Related Multi-Modal Feature Learning基于空间相关多模态特征学习的rgb-d场景识别.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing dataX-modalnet: 用于遥感数据分类的半监督深度跨模态网络OAAI
OAAI分享收藏Pansharpening for Cloud-Contaminated Very High-Resolution Remote Sensing Images云污染的高分辨率遥感影像的泛锐化
PREAI
分享收藏A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification用于遥感场景分类的特征聚合卷积神经网络
PREAI
分享收藏A deep translation (GAN) based change detection network for optical and SAR remote sensing images
OAAI
OAAI分享收藏
查看更多内容