Remote sensing scene classification aims to assign correct semantic labels to remote sensing images. Many state-of-the-art algorithms have made significant contributions to improving model accuracy. However, these algorithms often involve a large number of parameters and floating-point computations. To address this, we propose a lightweight network architecture named RandommaskFormer. This network initially utilizes random position filtering to reduce model complexity. Next, effective feature interactions are achieved through feature covariance analysis. Finally, we implement a two-stage optimization strategy, incorporating both label optimization and loss function optimization. Among the above strategy, we first perform clustering analysis on remote sensing image features using a KD tree to assign new visual labels. Then, we combine triplet loss with cross-entropy loss to guide model training. Experimental results on three mainstream datasets demonstrate the effectiveness of RandommaskFormer. Additionally, the deployment of the two-stage optimization strategy further improves the model's performance.

Noise interference often occurs, during the collection of point cloud data, which can significantly affect the original color characteristics of the data. Currently, point cloud color denoising methods are usually based on graph structures or filters, whose denoising effects are generally influenced by the construction of the graph or the choice of filter. Different from them, this paper proposes a deep learning network for point cloud color denoising that combines an implicit neural autoencoder with a multi-frame sparse convolutional learning. We propose a novel sparse feature extraction module based on sparse convolution. The use of sparse convolution allows only processing the features at the effective positions of the point cloud, significantly improving computational efficiency. Moreover, considering that valuable information can gradually be diluted through sparse convolution, a pretrained implicit neural autoencoder is employed to map the input point cloud features to a more informative high-dimensional space, allowing for more thorough network learning. Furthermore, we propose a new method for processing point cloud data that compacts the point cloud while retaining all its color features. Finally, to further utilize the spatiotemporal information of the point cloud, a multi-frame fusion algorithm is also proposed. Experimental results show that compared with state-of-the-art algorithms, our proposed method achieves better denoising performance where the average PSNR of denoised point cloud is improved up to 1.14 dB.