Joint classification using multisource remote sensing data for Earth observation is promising but challenging. Due to the gap of imaging mechanism and imbalanced information between multisource data, integrating the complementary merits for interpretation is still full of difficulties. In this article, a classification method based on asymmetric feature fusion, named asymmetric feature fusion network (AsyFFNet), is proposed. First, the weight-share residual blocks are utilized for feature extraction while keeping separate batch normalization (BN) layers. In the training phase, redundancy of the current channel is self-determined by the scaling factors in BN, which is replaced by another channel when the scaling factor is less than a threshold. To eliminate unnecessary channels and improve the generalization, a sparse constraint is imposed on partial scaling factors. Besides, a feature calibration module is designed to exploit the spatial dependence of multisource features, so that the discrimination capability is enhanced. Experimental results on the three datasets demonstrate that the proposed AsyFFNet significantly outperforms other competitive approaches.

Due to the limitations of single-source data, joint classification using multisource remote sensing data has received increasing attention. However, existing methods still have certain shortcomings when faced with feature extraction from single-source data and feature fusion between multisource data. In this article, a method based on multiscale interactive information extraction (MIFNet) for hyperspectral and synthetic aperture radar (SAR) image classification is proposed. First, a multiscale interactive information extraction (MIIE) block is designed to extract meaningful multiscale information. Compared with traditional multiscale models, it can not only obtain richer scale information but also reduce the model parameters and lower the network complexity. Furthermore, a global dependence fusion module (GDFM) is developed to fuse features from multisource data, which implements cross attention between multisource data from a global perspective and captures long-range dependence. Extensive experiments on the three datasets demonstrate the superiority of the proposed method and the necessity of each module for accuracy improvement.

Due to rich spectral and spatial information, the combination of hyperspectral and multispectral images (MSIs) has been widely used for Earth observation, such as wetland classification. However, mining of meaningful features and effective fusion of multisource remote sensing data are still urgent problems to be solved. In this article, graph-feature-enhanced selective assignment network (GSANet) is proposed. On the one hand, a graph feature extraction module (GFEM) is designed to extract topological structure information and combine with the rich spectral–spatial information. In particular, the features obtained by convolution are first mapped to the graph feature space, and the graph convolution operation is used to achieve propagation between nodes for preserving topological structure information. Moreover, to reduce the difference of graph features resulting from the mapping function and better explore the complementary properties of multisource data, a novel graph fusion strategy-graph dependence fusion is designed. A transition graph is generated to enhance the association and interaction between different graph features, so as to avoid the information loss caused by simple fusion operation. On the other hand, a selective feature assignment module (SFAM) is developed to adaptively assign weights to different discriminative features. SFAM assigns weights to different features to selectively emphasize informative features and suppress less useful ones. Extensive experiments are conducted on two multisource remote sensing datasets, and the improvement of at least 1.27% and 0.98% compared to other state-of-the-art work demonstrates the superiority of the proposed GSANet.

Hyperspectral and multispectral images (HS/MS) fusion and classification as an important branch of data quality improvement and interpretation, has attracted increasing attention in recent years. However, the unavailable sensor prior still limits the performance of many traditional fusion methods, consequently deteriorating the classification results. Despite the unsupervised methods based on convolutional neural network (CNN) making a lot of attempts to mitigate the limitations, challenges with extracting the long-range dependencies hamper the performance. To address these impediments, a transformer-based baseline constructed by the cross-scale mixing attention (CSMFormer) is designed for HS/MS fusion and classification. Especially, the spatial-spectral mixer (SSMixer) is utilized to extract the long-range dependencies at large scale. Simultaneously, cross-scale feature calibration is achieved by combining information from the original scale. After that, nonlinear enhancement module (NLEM) is designed to encourage feature discrimination. Note that the spatial and spectral mixers can be replaced by any spatial-spectral feature extractors. Therefore, the proposed CSMFormer is flexible in data fusion, land-covers classification, segmentation, etc. Experiments about data fusion and land-covers classification on two HS/MS wetland remote sensing scenes demonstrate the superiority of the proposed CSMFormer baseline, improving the data quality and classification precision.

Advances in multisource remote sensing have allowed for the development of more comprehensive observation. The adoption of deep convolutional neural networks (CNN) naturally includes spatial-spectral information, which has achieved promising performance in multisource data classification. However, challenges are still found with the extraction of spatial distribution and spectrum relationships, which eventually limit the classification performance. To solve the issue, a spatial-spectral perception network (S2PNet) is proposed to extract the advantages of different data sources and the cross information between data sources in a targeted manner. Specifically, the spatial perception network is developed to build the spatial distribution relationship from high-resolution images, while the spectral perception network extracts the spectrum relationship from spectral images. For perceiving cross information, a memory unit is utilized to store the features from different data sources in succession. In addition, the distance loss and reconstruction loss are introduced to keep the feature integrity, and the cross-entropy loss ensures that features can distinguish different classes. The comprehensive experiments are conducted on several datasets to validate the superiority of the proposed algorithm. The proposed S2PNet outperforms the considered classifiers with an average improvement of +0.77%, +5.62%, +1.58%, and +1.79% for overall accuracy values.

Convolutional neural networks (CNN) have attracted increasing attention in the field of multimodal cooperation. Recently, the adoption of CNN-based methods has achieved remarkable performance in multisource remote sensing data classification. However, it is still confronted with challenges in the aspect of complementarity extraction. In this paper, the adversarial complementary learning strategy is embedded into the CNN model called ACL-CNN, which is employed to extract the complementary information of the multisource data. The proposed ACL-CNN is able to filter out the common patterns and specific patterns from multisource data by conducting the adversarial max-min game. Especially, the modality-independent common patterns constitute the basic representation of the land-covers, while the specific patterns that are linearly independent of the common patterns that provide the supplementary representation. Therefore, the complementary information is mapped to a compact and discriminative representation. To eliminate the singularity noise, a learnable pattern sampling module (PSM) is designed to extract the mutual-exclusion relationship between specific patterns. Extensive experiments over three datasets demonstrate the superiority of the proposed ACL-CNN compared with several classification technologies.