Zhang, J.; Lei, J.; Xie, W.; Li, D. Invariant Attribute-Driven Binary Bi-Branch Classification of Hyperspectral and LiDAR Images. Remote Sens.2023, 15, 4255.
Zhang, J.; Lei, J.; Xie, W.; Li, D. Invariant Attribute-Driven Binary Bi-Branch Classification of Hyperspectral and LiDAR Images. Remote Sens. 2023, 15, 4255.
Zhang, J.; Lei, J.; Xie, W.; Li, D. Invariant Attribute-Driven Binary Bi-Branch Classification of Hyperspectral and LiDAR Images. Remote Sens.2023, 15, 4255.
Zhang, J.; Lei, J.; Xie, W.; Li, D. Invariant Attribute-Driven Binary Bi-Branch Classification of Hyperspectral and LiDAR Images. Remote Sens. 2023, 15, 4255.
Abstract
Hyperspectral image and LiDAR image fusion plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, like Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we proposed a invariant attribute-driven binary bi-branch classification (IABC) method which is a unified network that combines binary Convolutional Neural Network (CNN) and GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structured representation of HSI and LiDAR images in the spectral-spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision-making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrates the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.
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