Single-modal image registration methods are generally not feasible for visible and infrared images. Besides, multi-modal image registration methods still suffer from uneven distribution of extracted features, low repeatability, and ambiguous features. To address these issues, a coarse-to-fine infrared and visible image registration approach for dual sensor UAV imaging system is proposed, which is resilient to the difference of focal lengths and field of view. First, in the coarse registration step, the infrared image is transformed to the same scale as the visible image by using the similarity transformation. This operation makes the proposed method robust to the variation of field of view. Then, the feature point pairs are initialized using feature detectors in the infrared image’s blocked phase congruency feature map. Next, the feature point pairs are optimized by estimating the offset based on the relationship between the constructed feature descriptors. Finally, using elastic deformation, the pixel-level registered infrared image is obtained. Extensive experiments demonstrate the superior performance of the proposed coarse-to-fine image registration methodology in the real infrared-visible image pairs. The code and dataset are available at https://drive.google.com/drive/folders/1mpUWwHUbKTrBdOrNMNRRnuJclDUAC7nU?usp=sharing.

Heavy rains usually hit Laos countrywide and cause serious floods, influencing local agriculture, households, and the economy. Therefore, it is crucial to monitor the flooding in Laos to better understand the flood patterns and characteristics. This paper aims to analyze the influence of the flooding in Laos with multi-source data, e.g., Synthetic Aperture Radar (SAR), optical multi-spectral images, and geographic information system data. First, the flood areas in Laos from 2018 to 2022 are detected using a decision fusion method. Based on the flood areas and the global Land Use/Land Cover (LULC) product, the macro scale global impact of the flood is analyzed. Second, taking the Vientiane Capital as a case study area, a flood forecasting method is applied to estimate the risk of flooding. Finally, optical images before and after the flood event are extracted for a close-up comparison at the micro scale. Based on the above multi-scale analysis, floods in Laos are found to be predominantly concentrated in the flat areas near the Mekong River, with a decreasing trend over time, which could be helpful for flood management and mitigation strategies in Laos. The validation results exhibited notable average indices across a five-year period, with mIoU (0.7782), F1 score (0.7255), and overall accuracy (0.9854), respectively.

With the improvement of imaging equipment, hyperspectral underwater target detection (HUTD) has raised much interest in recent years. The existing HUTD methods do not fully utilize spectral characteristics and need prior information about targets. Besides, the detection performance lacks verification in natural scenarios. In this paper, the authors propose a Feature Bands based Unsupervised underwater target Detection method (FBUD), which aims at finding the optimal feature bands to identify the underwater target near the coastline. Specifically, the normalized difference water index (NDWI) and unmixing technique are adopted to find the target and background pixels. Then, the spectral difference between the target and background is used to find the feature bands. With a simple and fast math operation of the feature bands, the probability map of the underwater target can be easily obtained. Besides, a new unmanned aerial vehicle (UAV)-borne hyperspectral image dataset named HNU-UTD is built for underwater target detection in real-world scenes. Experimental results obtained with the HNU-UTD dataset confirm the accuracy and effectiveness of the proposed detection method, which even outperforms supervised detection methods.

Hyperspectral images have been widely used for target detection due to their abundant spatial and spectral information. In this paper, a shadow-insensitive hyperspectral target detection framework based on exposure fusion is proposed, which consists of the following major steps. First, the input hyperspectral image is divided into two parts, namely the shadow region and the non-shadow region. Second, total variation-based feature extraction and over-exposure operation are performed on the input image to produce two feature images, i.e., the original feature image and the over-exposure image. Third, a self-guided constrained energy minimization (SGCEM) detector is performed on the two feature images to detect the targets in shadow and non-shadow regions, respectively. Finally, the detection results obtained on the original feature image and the over-exposure image are fused to acquire the final detection result. Extensive experiments conducted on real-world data illustrate that the proposed method can achieve satisfactory results when shadow exists.

In recent years, remote sensing (RS) data have become increasingly diversified due to the continuous innovation of sensors, communications, computers, and other technologies. The use of multimodal data for Earth observation missions has become a crucial research topic. Compared with single-source RS data, the fusion of multisouce RS data can obtain more comprehensive information for categorizing scenes. However, multisource RS images fusion classification usually requires complex feature extraction and fusion, building a suitable network complexity to facilitate heterogeneous information exchange and avoid substantial redundancy is a significant challenge. To overcome these limitations, we introduce a lightweight dual-branch soft-attention classification framework, which designs the multiscale feature interaction module for collaborative HSI-LiDAR classification. Compared with other cutting-edge models, the proposed framework is compact and deeply integrates multimodality heterogeneous characteristics. The multiscale feature interaction module consists of a multiscale information fusion pattern and a soft attention module, which can effectively extract hierarchical information and improve the heterogeneous feature representation. In addition, the Transformer module adopts weight-sharing to process features from different branches, effectively improve both parameter reduction and the powerful modeling capability of long-range dependencies. To validate the efficacy and advantages of our proposed framework, extensive experiments were performed across three datasets. The results indicate superior performance compared to current state-of-the-art approaches.

The collaborative classification of hyperspectral images (HSI) with a complementary modality such as light detection and ranging (LiDAR) or synthetic aperture radar (SAR), which is known as multi-source remote sensing image classification (MRSIC), can combine rich spectral information with structural cues and is helpful for the recognition of complex ground objects. However, effectively incorporating these structural cues into multi-level spectral-spatial features and modeling their spatial relationships remain challenging under limited training samples. To address these problems, a multi-level convolutional collaborative classification network based on the DefMamba fusion mechanism (DMFNet) is proposed in this paper. First, monogenic phase congruency (MPC) responses are added to the complementary modality to enhance its structural information. Then, a dual-branch feature extraction module is constructed to derive multi-level features from HSI and the enhanced complementary modality and aggregate them into a joint representation. Finally, a DefMamba fusion module applies deformable selective scanning to the aggregated features, so as to adaptively model spatial dependencies according to the fused content. Experiments on three public multimodal remote sensing datasets, covering both HSI–LiDAR and HSI–SAR settings, demonstrate that DMFNet achieves the highest mean overall accuracy among the compared methods with 10 labeled training samples per class, with the largest improvement on the HSI-SAR scene. Additional experiments on Houston 2018 further evaluate its classification performance on a large-scale scene.