Synthetic aperture radar (SAR) and optical images provide highly complementary ground information. The fusion of SAR and optical data can significantly enhance semantic segmentation inference results. However, the fusion methods for multimodal data remains a challenge for current research due to significant disparities in imaging mechanisms from diverse sources. Our goal was to bridge the significant gaps between optical and SAR images by developing a dual-input model that utilizes image-level fusion. To improve most existing state-of-the-art image fusion methods, which often assign equal weights to multiple modalities, we employed the principal component analysis (PCA) transform approach. Subsequently, we performed feature-level fusion on shallow feature maps, which retain rich geometric information. We also incorporated a channel attention module to highlight channels rich in features and suppress irrelevant information. This step is crucial due to the substantial similarity between SAR and optical images in shallow layers such as geometric features. In summary, we propose a generic multimodal fusion strategy that can be attached to most encoding–decoding structures for feature classification tasks, designed with two inputs. One input is the optical image, and the other is the three-band fusion data obtained by combining the PCA component of the optical image with the SAR. Our feature-level fusion method effectively integrates multimodal data. The efficiency of our approach was validated using various public datasets, and the results showed significant improvements when applied to several land cover classification models.

As an important and fundamental step in 3D reconstruction, point cloud registration aims to find rigid transformations that register two point sets. The major challenge in point cloud registration techniques is finding correct correspondences in the scenes that may contain many repetitive structures and noise. This paper is primarily concerned with improving registration using a priori semantic information in the search for correspondences. In particular, we present a new point cloud registration pipeline for large, outdoor scenes that takes advantage of semantic segmentation. Our method consisted of extracting semantic segments from point clouds using an efficient deep neural network, then detecting the key points of the point cloud and using a feature descriptor to get the initial correspondence set, and, finally, applying a Random Sample Consensus (RANSAC) strategy to estimate the transformations that align segments with the same labels. Instead of using all points to estimate a global alignment, our method aligned two point clouds using transformations calculated by each segment with the highest inlier ratio. We evaluated our method on the publicly available Whu-TLS registration data set. These experiments demonstrate how a priori semantic information improves registration in terms of precision and speed.

With the rapid development of 3D scanning technologies, high-density point clouds of cultural heritage artifacts such as stone carvings, statues pose significant challenges in storage, processing, and accurate reconstruction. This paper proposes a point cloud simplification method tailored for cultural heritage applications, combining clustering and saliency analysis to preserve intricate surface details critical for archaeological studies. By segmenting point clouds into clusters with normal vector constraints and evaluating saliency through roughness and curvature metrics, our method adaptively retains primary features including engraved patterns weathered textures while simplifying non-feature regions. Experiments on stone carvings from the Northern Song Imperial Mausoleum, Terracotta Warriors, and Stanford datas demonstrate that the algorithm effectively avoids mesh holes and maintains geometric fidelity, enabling efficient 3D reconstruction for heritage conservation. This work bridges advanced point cloud processing with practical archaeological needs, offering a robust tool for digitizing and analyzing cultural relics with minimal loss of historically significant details.