Research on 3D Fast Reconstruction and Automatic Target Recognition of Multi-source Satellite Remote Sensing Data for Engineering Facilities

This research project tackles the challenges of fast spatial modeling and automated object identification in large engineering environments using satellite remote‑sensing data from multiple sources. By combining optical, synthetic aperture radar (SAR), and multispectral satellite images, the method creates a solid framework for quick 3D reconstruction and automatic target recognition (ATR) of important engineering facilities. The framework uses advanced data‑fusion techniques to line up different sensor inputs, reducing spatial and temporal mismatches. A deep‑learning pipeline speeds up the 3D reconstruction, employing neural radiance fields or multi‑view stereo matching that are tuned for satellite geometry. At the same time, an attention‑guided convolutional neural network is built for high‑accuracy ATR, allowing classification

and location of complex structures under varying environmental conditions. The integrated system is tested on standard remote‑sensing datasets, measuring reconstruction speed, geometric accuracy, and target‑recognition precision. The work provides practical implementation support and methodological guidance for structural monitoring, disaster assessment, and urban planning, offering a scalable way to process high‑resolution multi‑source satellite observations.

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