As a list of remotely sensed data sources is available, the effective processing of remote sensing images is of great significance in practical applications in various fields. This paper proposes a new lightweight network to solve the problem of remote sensing image processing by using the method of deep learning. Specifically, the proposed model employs ShuffleNet V2 as the backbone network, appropriately increases part of the convolution kernels to improve the classification accuracy of the network, and uses the maximum overlapping pooling layer to enhance the detailed features of the input images. Finally, Squeeze and Excitation (SE) blocks are introduced as the attention mechanism to improve the architecture of the network. Experimental results based on several multisource data show that our proposed network model has a good classification effect on the test samples and can achieve more excellent classification performance than some existing methods, with an accuracy of 91%, and can be used for the classification of remote sensing images. Our model not only has high accuracy but also has faster training speed compared with large networks and can greatly reduce computation costs. The demo code of our proposed method will be available at https://github.com/li-zi-qi.

Given the merits in high computational efficiency and low storage cost, hashing techniques have been widely studied in cross-media retrieval. Existing methods usually adopt the equal length encoding scheme to represent the multimedia data. However, the strictly equal length scheme maybe not optimal because the dimension of different modalities is often various. Besides, there exists other challenges in designing a cross-media retrieval system, e.g., how to address the discrete constraints, how to avoid using the n*n similarity matrix, and how to effectively exploit the discriminative label information. To conquer the above challenges, we propose a novel method, i.e., discrete asymmetric hashing (DAH). Specifically, DAH exploits a flexible model, which can seamlessly deal with equal or unequal length encoding scenarios. Moreover, DAH constructs a supervised semantic embedding framework by jointly minimizing the distance-distance difference and label reconstructing error, significantly reducing the computational complexity. An asymmetric strategy is employed to establish the connection between hash codes and the latent subspace. Furthermore, the hash codes can be learned discretely by the designed optimization algorithm. In the training stage2, a semantic intersection scheme is proposed to learn more powerful hash functions. Experiments show that our DAH is effective in equal and unequal scenarios.

3D Morphable Face Models (3DMM) have been used in pattern recognition for some time now. They have been applied as a basis for 3D face recognition, as well as in an assistive role for 2D face recognition to perform geometric and photometric normalisation of the input image, or in 2D face recognition system training. The statistical distribution underlying 3DMM is Gaussian. However, the single-Gaussian model seems at odds with reality when we consider different cohorts of data, e.g. Black and Chinese faces. Their means are clearly different. This paper introduces the Gaussian Mixture 3DMM (GM-3DMM) which models the global population as a mixture of Gaussian subpopulations, each with its own mean. The proposed GM-3DMM extends the traditional 3DMM naturally, by adopting a shared covariance structure to mitigate small sample estimation problems associated with data in high dimensional spaces. We construct a GM-3DMM, the training of which involves a multiple cohort dataset, SURREY-JNU, comprising 942 3D face scans of people with mixed backgrounds. Experiments in fitting the GM-3DMM to 2D face images to facilitate their geometric and photometric normalisation for pose and illumination invariant face recognition demonstrate the merits of the proposed mixture of Gaussians 3D face model.