psPRF:Pansharpening Planar Neural Radiance Field for Generalized 3D Reconstruction Satellite Imagery

• URL: http://arxiv.org/abs/2406.15707v1
• Date: Sat, 22 Jun 2024 02:02:32 GMT
• Title: psPRF:Pansharpening Planar Neural Radiance Field for Generalized 3D Reconstruction Satellite Imagery
• Authors: Tongtong Zhang, Yuanxiang Li,
• Abstract summary: Most current NeRF variants for satellites are designed for one specific scene and fall short of generalization to new geometry. This paper introduces psPRF, a Planar Neural Radiance Field designed for paired low-resolution RGB (LR-RGB) and high-resolution panchromatic (HR-PAN) images from satellite sensors with Rational Polynomial Cameras (RPC) To support the generalization ability of psRPF across scenes, we adopt projection loss to ensure strong geometry self-supervision.
• Score: 0.6445605125467574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most current NeRF variants for satellites are designed for one specific scene and fall short of generalization to new geometry. Additionally, the RGB images require pan-sharpening as an independent preprocessing step. This paper introduces psPRF, a Planar Neural Radiance Field designed for paired low-resolution RGB (LR-RGB) and high-resolution panchromatic (HR-PAN) images from satellite sensors with Rational Polynomial Cameras (RPC). To capture the cross-modal prior from both of the LR-RGB and HR-PAN images, for the Unet-shaped architecture, we adapt the encoder with explicit spectral-to-spatial convolution (SSConv) to enhance the multimodal representation ability. To support the generalization ability of psRPF across scenes, we adopt projection loss to ensure strong geometry self-supervision. The proposed method is evaluated with the multi-scene WorldView-3 LR-RGB and HR-PAN pairs, and achieves state-of-the-art performance.

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