Jiao Shi

Band selection (BS) is commonly utilized to reduce the dimensionality of hyperspectral images (HSI). Some semi-supervised BS methods achieve great classification performance using limited labeled samples. However, most methods rely on a unified discriminant learner to find the band subset, which lacks consideration for the complex feature representation of HSI data. This paper proposes a semi-supervised adaptive BS method based on evolutionary multitasking to effectively explore the diverse intrinsic characteristics. The BS problem is modeled as a multitask optimization problem where multiple BS tasks jointly select optimal band subset based on different discriminant learners. To achieve the collaborative search of these BS tasks, a structure-aware evolutionary multitasking approach is proposed, which generates the individuals with various number of bands by considering the low redundancy and complete spectral structure information, and leverages the specially designed genetic operations to facilitate the population evolution under the structure preservation constraint. A dynamic interaction mechanism is devised to adjust task interaction intensity adaptively to enhance effective knowledge sharing between tasks. Additionally, a discriminability evaluation criterion is proposed to mitigate noisy sample interference by integrating sample reliability and class separability. Experiments have demonstrated that the proposed method effectively explores the complex feature representation of HSI data through joint discriminant learners.

In recent years, deep learning (DL) has achieved remarkable success in classifying hyperspectral images (HSIs), relying heavily on the quantity and quality of labeled samples. However, obtaining sufficient labels for HSIs poses a challenge. HSIs obtained by the same sensor often exhibit similar spectral information due to their shared physical, chemical properties, or reflective attributes. Joint analysis of several HSIs enables the integration of limited labeled samples and extraction of more robust and discriminative features from different HSIs. Therefore, a multitask collaborative network (MTCN) for the joint classification of multiple HSIs acquired by the same sensor in different areas is proposed. In the MTCN, each HSI has its own feature extraction channel, which facilitates the learning of image-specific representations. In addition, a feature sharing channel (FSC) is created to extract and transfer multihierarchical image-shared representations between multiple HSIs, thereby forming a common knowledge pool to facilitate feature sharing. Furthermore, a cross-channel mutual attention module (CMAM) is designed to collaboratively utilize features from image-specific and image-shared channels, enhancing the efficiency of information communication in HSIs. The experimental results on six HSIs demonstrate that the proposed MTCN can jointly classify multiple HSIs by the same sensor in different areas and achieve good classification performance.

Point cloud registration is a hot research topic in the field of computer vision. In recent years, the registration method based on evolutionary computation has attracted more and more attention because of its robustness to initial pose and flexibility of objective function design. However, most of the current evolutionary computation-based point cloud registration methods do not take into account the multi-view problem, that is, to capture the close relationship between point clouds from different perspectives. We fully realize that if these relations are used correctly, the registration performance can be improved. Therefore, this paper proposes an evolutionary multitasking multi-view point cloud registration method, which solves the problem of multi-view error accumulation. To ensure the unity of global and local, a two-level knowledge transfer strategy is proposed, which divides the multi-view cloud registration task into two levels. This strategy unifies the search space of two registration tasks, solves the negative transfer phenomenon, and avoids the problem of falling into the local optimum. Finally, the effectiveness of the method is verified by sufficient experiments. This method has strong robustness to noise and outliers, and can be effectively implemented in various registration scenarios.

Unsupervised domain adaptation (UDA) is widely utilized to accomplish the visual object recognition task in the field of consumer electronics, aiming at reducing labeling costs in new scenarios. To address the error accumulation problem caused by unreliable target pseudo labels, existing feature-based UDA methods utilize the predicted posterior probability of target sample to model the uncertainty relationship between samples. However, due to differences in data characteristics of each class, unified uncertainty modeling may lead to information loss in some classes due to excessive fuzziness. In view of this, this paper proposes a uncertainty feature learning method with personalized class description for UDA (UFL-PCD) to accomplish the visual object recognition task. Firstly, to learn a more robust discriminative common feature subspace, the proposed method performs more flexible uncertainty modeling of relationships between intra-domain and cross-domain samples based on the personalized uncertainty description of each class. Secondly, to alleviate the error accumulation problem, a hybrid label filtering strategy is proposed to determine the credible target pseudo labels based on fuzzy membership degrees and adaptive rough approximations. Furthermore, a consensus prototype-driven prediction model is designed to predict target data by comprehensively considering the guidance information of the source domain and the intrinsic structural information of the target domain. Experiments conducted on four benchmark datasets have demonstrated that UFL-PCD can construct a common feature subspace with strong stability and adaptability through uncertainty feature learning.

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this paper proposes self-guided autoencoders (SGAE) for unsupervised change detection in heterogeneous remote sensing images. Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudo-labeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudo-labeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method.