Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification

• URL: http://arxiv.org/abs/2505.04003v1
• Date: Tue, 06 May 2025 22:30:23 GMT
• Title: Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification
• Authors: Feng Gao, Sheng Liu, Chuanzheng Gong, Xiaowei Zhou, Jiayi Wang, Junyu Dong, Qian Du,
• Abstract summary: Multi-source remote sensing data joint classification aims to provide accuracy and reliability of land cover classification.<n>Existing methods confront two challenges: inter-frequency multi-source feature coupling and inconsistency of complementary information exploration.<n>We present a Prototype-based Information Compensation Network (PICNet) for land cover classification based on HSI and SAR/LiDAR data.
• Score: 56.065032039986725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-source remote sensing data joint classification aims to provide accuracy and reliability of land cover classification by leveraging the complementary information from multiple data sources. Existing methods confront two challenges: inter-frequency multi-source feature coupling and inconsistency of complementary information exploration. To solve these issues, we present a Prototype-based Information Compensation Network (PICNet) for land cover classification based on HSI and SAR/LiDAR data. Specifically, we first design a frequency interaction module to enhance the inter-frequency coupling in multi-source feature extraction. The multi-source features are first decoupled into high- and low-frequency components. Then, these features are recoupled to achieve efficient inter-frequency communication. Afterward, we design a prototype-based information compensation module to model the global multi-source complementary information. Two sets of learnable modality prototypes are introduced to represent the global modality information of multi-source data. Subsequently, cross-modal feature integration and alignment are achieved through cross-attention computation between the modality-specific prototype vectors and the raw feature representations. Extensive experiments on three public datasets demonstrate the significant superiority of our PICNet over state-of-the-art methods. The codes are available at https://github.com/oucailab/PICNet.

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