Hyperspectral image (HSI) provides detailed spectral and spatial information, essential for precise earth observation and various applications. Deep learning has advanced HSI classification, but the scarcity of labeled data and large model parameters necessitate semi-supervised methods to enhance performance and generalization. In this paper, we propose a novel semi-supervised framework dubbed Knowledge-Aware Geometric Contourlet Semantic Learning (KGCSL), aiming to achieve high-precision HSI classification with limited samples leveraging geometric and semantic knowledge. Specifically, to fully leverage geometric knowledge, KGCSL incorporates multi-scale and multi-directional representations of the contourlet transform within the neural network, enhancing the robustness of feature extraction and interpretability. Furthermore, to fully utilize semantic knowledge, an entropy-weighted prototype loss function is designed that exploits the attribute relationships between labeled and unlabeled samples to guide the optimization of unlabeled samples, promoting comprehensive semantic learning. Comprehensive evaluations of the proposed KGCSL framework on three public HSI datasets show that it outperforms existing state-of-the-art HSI classification methods and exhibits excellent generalization capabilities in limited-sample scenarios. The source code is available athttps://github.com/ShirlySmile/KGCSL.

Multisource optical remote sensing (RS) image classification has obtained extensive research interest with demonstrated superiority. Existing approaches mainly improve classification performance by exploiting complementary information from multisource data. However, these approaches are insufficient in effectively extracting data features and utilizing correlations of multisource optical RS images. For this purpose, this article proposes a generalized spatial-spectral relation-guided fusion network (S2RGF-Net) for multisource optical RS image classification. First, we elaborate on spatial- and spectral-domain-specific feature encoders based on data characteristics to explore the rich feature information of optical RS data deeply. Subsequently, two relation-guided fusion strategies are proposed at the dual-level (intradomain and interdomain) to integrate multisource image information effectively. In the intradomain feature fusion, an adaptive de-redundancy fusion module (ADRF) is introduced to eliminate redundancy so that the spatial and spectral features are complete and compact, respectively. In interdomain feature fusion, we construct a spatial-spectral joint attention module (SSJA) based on interdomain relationships to sufficiently enhance the complementary features, so as to facilitate later fusion. Experiments on various multisource optical RS datasets demonstrate that S2RGF-Net outperforms other state-of-the-art (SOTA) methods.

The progress of brain cognition and learning mechanisms has provided new inspiration for the next generation of artificial intelligence (AI) and provided the biological basis for the establishment of new models and methods. Brain science can effectively improve the intelligence of existing models and systems. Compared with other reviews, this article provides a comprehensive review of brain-inspired deep learning algorithms for learning, perception, and cognition from microscopic, mesoscopic, macroscopic, and super-macroscopic perspectives. First, this article introduces the brain cognition mechanism. Then, it summarizes the existing studies on brain-inspired learning and modeling from the perspectives of neural structure, cognitive module, learning mechanism, and behavioral characteristics. Next, this article introduces the potential learning directions of brain-inspired learning from four aspects: perception, cognition, understanding, and decision-making. Finally, the top-ten open problems that brain-inspired learning, perception, and cognition currently face are summarized, and the next generation of AI technology has been prospected. This work intends to provide a quick overview of the research on brain-inspired AI algorithms and to motivate future research by illuminating the latest developments in brain science.

Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients tamper their model parameters to deteriorate the global model. Existing methods for defending against poisoning attacks primarily rely on identifying malicious clients, but struggle to balance robustness and efficiency. To address these issues, we propose FedPTA, a Prior-based Tensor Approximation (PTA) method. The core idea of FedPTA is to detect malicious clients in federated learning by leveraging inherent priors. This method initially innovatively defines multi-round model parameters as a three-dimensional tensor and unfolds it along different dimensions. Subsequently, three inherent priors - the similarity among benign clients, the continuity of multi-round client model parameters and the sparsity of malicious parameters, are integrated into a convex optimization framework. Through the optimization process, the optimal solutions for the background tensor and anomaly tensor are solved. Ultimately, the anomaly tensor is used to highlight the element-level features of malicious parameters, effectively distinguishing malicious clients. Evaluative studies supported by theoretical significance demonstrate the effectiveness of FedPTA, outperforming current state-of-the-art methods in terms of detection accuracy and computational efficiency.

Multisource remote sensing image fusion classification aims to produce accurate pixel-level classification maps by combining complementary information from different sources of remote sensing data. Existing methods based on Convolutional Neural Networks (CNN-based) utilize a patch-based learning framework, which has a high computational cost, leading to poor real-time performance. In contrast, methods based on Fully Convolutional Networks (FCN-based) can process the entire image directly, achieving fast inference. However, FCN-based methods require high computational resources and exhibit shortcomings in feature fusion, hindering practical applications. In this paper, a lightweight FCN-based Progressive Hierarchical Fusion Network (PHFNet) is tailored for multisource remote sensing image classification. PHFNet comprises a pyramid dual-path encoder and a pyramid decoder. In the encoder, cross-source features are hierarchically fused via the adaptive modulation fusion module (AMF), which leverages style calibration for cross-source alignment and promotes the complementarity of the fusion feature. In the decoder, we introduced an improved convolutional gated recurrent unit (iConvGRU) to progressively integrate the semantic and detailed information of hierarchical features, producing a context-enhanced global representation. In addition, we consider the relation between the channel number, convolutional kernel size, and parameter count to make the model as lightweight as possible. Comprehensive evaluations on three multisource remote sensing datasets demonstrate that PHFNet improves overall accuracy by 1.5% to 2.8% with a low computational overhead compared to state-of-the-art methods. The source code is avaliable athttps://github.com/ShirlySmile/PHFNet.

Multisource remote sensing images provide complementary multidimensional information for reliable and accurate classification. However, gaps in imaging mechanisms result in heterogeneity between multiple source images. During fusion, this heterogeneity causes the generated multisource representations may be redundant and ignore discriminative uni-source information, which significantly hampers the fusion classification performance. To address this challenge, we introduce a novel multisource joint representation learning method for remote sensing image fusion classification, termed Multisource Information Bottleneck Fusion Network (MIBF-Net). Based on the Information Bottleneck principle, MIBF-Net employs mutual information constraints to effectively integrate multisource information, generating a comprehensive and non-redundant multisource representation. Specifically, MIBF-Net first introduces an attribution-driven noise adaptation layer to dynamically balance the speed of feature learning across sources for extracting discriminative uni-source intrinsic information. Furthermore, a cross-source relationship encoding module is designed to fully explore cross-source complex dependencies for enhancing the richness of fused representations. Finally, we design an information bottleneck fusion module to fuse uni-source semantic information and cross-source information while reducing redundancy. In particular, we employ variational inference techniques to effectively address the mutual information optimization problem and provide theoretical derivations. Extensive experimental results on three heterogeneous multisource remote sensing data benchmarks show that the model significantly outperforms the state-of-the-art methods.

The international charity SolarAid aims to provide access to lights in areas without electricity, and it is a challenge to accurately and efficiently transmit the lights to the areas in need. Multisource, multitemporal, and multimodal remote sensing images can provide rich information about the target area, so using multisource remote sensing images for accurate detection of human settlements without electricity is a feasible solution. In this paper two separate detection tasks are formulated: building two attention SENet for settlements detection and light detection using the Sentinel-2 dataset and the Suomi Visible Infrared Imaging Radiometer Suite (VIIRS) night time dataset, respectively. In addition, we study a new outlier removal method based on the pixel distribution characteristics of the VIIRS dataset for data pre-processing, and propose a post-processing method based on region continuity for further correction of the results. Experiments show that our method can maximize the use of multisource data information and rank first in the detection of settlements without electricity challenge track (Track DSE) of the 2021 IEEE GRSS Data Fusion Contest.