In this paper, an Adaptive Fusion Transformer (AFT) is proposed for unsupervised pixel-level fusion of visible and infrared images. Different from the existing convolutional networks, transformer is adopted to model the relationship of multi-modality images and explore cross-modal interactions in AFT. The encoder of AFT uses a Multi-Head Self-attention (MSA) module and Feed Forward (FF) network for feature extraction. Then, a Multi-head Self-Fusion (MSF) module is designed for the adaptive perceptual fusion of the features. By sequentially stacking the MSF, MSA, and FF, a fusion decoder is constructed to gradually locate complementary features for recovering informative images. In addition, a structure-preserving loss is defined to enhance the visual quality of fused images. Extensive experiments are conducted on several datasets to compare our proposed AFT method with 21 popular approaches. The results show that AFT has state-of-the-art performance in both quantitative metrics and visual perception.

Autofocus plays a key role in synthetic aperture radar (SAR) imaging, especially for high-resolution imaging. In the literature, the minimum-entropy-based algorithms (MEA) have been proved to be robust and have been widely applied in SAR. However, this kind of method needs hundreds of iterations and is computationally expensive. In this paper, we proposed a non-iterative autofocus scheme based on deep learning and minimum-entropy criterion. It’s an unsupervised framework, which utilizes entropy as the loss function. In this scheme, deep neural networks are utilized for feature extraction and parameter estimation. Based on this scheme, two autofocus models (autofocus network and progressive autofocus network) are proposed. After training, the network learned the rules of autofocus from a large number of examples. Experimental results on real SAR data show that the proposed methods have focusing quality close to the state-of-the-art but with real-time focusing speed.

Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. However, existing autofocus algorithms either have poor focusing quality or a slow focusing speed. In this paper, an ensemble learning-based autofocus method is proposed. Convolutional Extreme Learning Machine (CELM) is constructed and utilized to estimate the phase error. However, the performance of a single CELM is poor. To overcome this, a novel, metric-based combination strategy is proposed, combining multiple CELMs to further improve the estimation accuracy. The proposed model is trained with the classical bagging-based ensemble learning method. The training and testing process is non-iterative and fast. Experimental results conducted on real SAR data show that the proposed method has a good trade-off between focusing quality and speed.

Aircraft identification has been a research hotspot in remote-sensing fields. However, due to the presence of clouds in satellite-borne optical imagery, it is difficult to identify aircraft using a single optical image. In this paper, a Multi-path Interactive Network (MIN) is proposed to fuse Optical and Synthetic Aperture Radar (SAR) images for aircraft identification on cloudy days. First, features are extracted from optical and SAR images separately by convolution backbones of ResNet-34. Second, a piecewise residual fusion strategy is proposed to reduce the effect of clouds. A plug-and-play Interactive Attention Sum-Max fusion module (IASM), is thus constructed to interact with features from multi-modal images. Moreover, multi-path IASM is designed to mix multi-modal features from backbones. Finally, the fused features are sent to the neck and head of MIN for regression and classification. Extensive experiments are carried out on the Fused Cloudy Aircraft Detection (FCAD) dataset that is constructed, and the results show the efficiency of MIN in identifying aircraft under clouds with different thicknesses.Compared with the single-source model, the multi-source fusion model MIN is improved by more than $20\%$, and the proposed method outperforms the state-of-the-art approaches.

Heterogeneous object-level change detection aims to detect the state of the objects and whether they have changed from multi-temporal multimodal data. In this paper, a new Cross-modal Interactive Change Detector (CICD) is proposed for object-level change detection from multi-temporal optical and Synthetic Aperture Radar (SAR) images. The CICD consists of a backbone, Cross-modal Interactive Module (CIM), neck, and head. CIM is designed to work with features extracted from modalities by the backbone network, enabling it to identify more changes in objects. Moreover, to address data imbalances in change categories caused by variations in satellite revisit cycles and aircraft flight plans, we introduce a Heterogeneous Class Balanced Module (HCBM). An Eliminate Adversarial Network (EAN) is constructed as the main component of the HCBM. It is used to eliminate objects to augment images in which objects appear in only one temporal instant, thus reducing imbalances in the dataset. Extensive experiments are conducted on the Multi-modal Object-level Change Dataset (MOCD), and the results show that CICD can achieve state-of-the-art performance.

Deep learning (DL) has been widely recognized for its strong feature representation capability, making it a promising technique to improve pansharpening methods. However, existing DL-based methods commonly extract the spectral information and spatial information from the high-resolution panchromatic (PAN) images and low-resolution multispectral (MS) images, respectively. This separation limits the effective extraction and integration of potential spectral-spatial information, ultimately reducing the quality of the generated high-resolution multispectral (HRMS) images. In this paper, we propose a novel spatialspectral dual guided network (SSDGN), aiming to fully capitalize on the spectral and spatial information contained in both the PAN and MS images. Firstly, to enhance feature extraction, we introduce two subnetworks: the progressive spectral feature extraction (PSpeFE) subnetwork and progressive spatial feature extraction (PSpaFE) for spatial features. These extract information from the PAN and MS images. Additionally, features from the frequency domain (FD) and intensity domain (ID) of both image types are leveraged to guide and enhance the efficiency of feature extraction. Then, a joint spatial-spectral attention feature fusion module and a multi-stage residual reconstruction module are devised to efficiently harness the extracted spatial and spectral information. Finally, extensive experiments are conducted to evaluate the performance and effectiveness of the proposed SSDGN. Compared to the second-best methods, our approach achieves an average reduction of 12.3% in ERGAS across three satellite datasets. The QNR metric improves by up to 2.1% and averages a 0.8% gain, demonstrating consistent advantages in spectral fidelity and fusion quality.