AI Article Synopsis
- Recent advancements in joint classification using multisource remote sensing data are addressing limitations in single-source data extraction.
- A new method called MIFNet is introduced, focusing on multiscale interactive information extraction to optimize hyperspectral and SAR image classification.
- Key innovations include a specially designed MIIE block for rich scale information and a global dependence fusion module that enhances feature fusion through cross attention, both contributing to improved model accuracy.
Article Abstract
Due to the limitations of single-source data, joint classification using multisource remote sensing data has received increasing attention. However, existing methods still have certain shortcomings when faced with feature extraction from single-source data and feature fusion between multisource data. In this article, a method based on multiscale interactive information extraction (MIFNet) for hyperspectral and synthetic aperture radar (SAR) image classification is proposed. First, a multiscale interactive information extraction (MIIE) block is designed to extract meaningful multiscale information. Compared with traditional multiscale models, it can not only obtain richer scale information but also reduce the model parameters and lower the network complexity. Furthermore, a global dependence fusion module (GDFM) is developed to fuse features from multisource data, which implements cross attention between multisource data from a global perspective and captures long-range dependence. Extensive experiments on the three datasets demonstrate the superiority of the proposed method and the necessity of each module for accuracy improvement.
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| http://dx.doi.org/10.1109/TNNLS.2022.3171572 | DOI Listing |
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