Due to the limitations of hyperspectral satellite imaging systems, hyperspectral images (HSIs) have a low spatial resolution and spatial missing regions, which hinders their full potential for land cover classification. Through collaboration with the auxiliary high-resolution multispectral image (MSI), the high-resolution HSI (HHSI) can be obtained. Although image fusion deep learning methods have emerged as the predominant approach for enhancing the spatial resolution of HSI, few studies have solved the problem of spatial missing. Moreover, the reliance on the spectral response function and the point spread function limits the applicability and performance of these methods. This study proposes an HSI cross-region reconstruction framework, which can not only fuse the HSI and MSI in overlapping regions but also be used to reconstruct the missing information of HSI in non-overlapping regions. The core of this framework is an unsupervised deep end-to-end fusion network with adaptive attention based on the conception of endmember and abundance. Specifically, a multi-layer attention mechanism with an interaction module is designed to enhance the extraction of endmember and abundance features. In addition, the attached module addresses the dependency on the prior information about degradation models. To verify the practicability of the proposed framework, we conduct evaluations using both simulation datasets and real on-board data for experiments involving both overlapping and non-overlapping regions. The experimental results demonstrate that the proposed method is effective and the reconstructed HHSI is reliable.

Laser image detection and ranging (LiDAR) data are used as an important source for monitoring the natural environment and environmental changes, forest management, transport, and urban development. Real-time LiDAR is especially useful in applications that require immediate feedback or decision-making, such as post-earthquake emergency analysis, autonomous vehicles, precision agriculture, military applications, and infrastructure monitoring. For instance, rapid and real-time assessment of structural damage following disasters like earthquakes ensures the effective use of resources by directing first responders to priority areas. We focused on minimizing the time losses in pre-flight preparation and post-flight data processing processes at the low-altitude LiDAR mapping. To eliminate time losses, LiDAR data, which can be accessed via a web browser, were visualized as a real-time 3D point cloud. The X3D file format was used in this visualization process. To realize the accessibility and sharing of the data, HTML5 technologies were used, and the data were transferred to the cloud system in real time. Thus, a user-friendly system was developed that can make these data available to researchers simultaneously. We designed and developed a platform-independent web-based real-time LiDAR (WebRT-LiDAR) mapping system. As an application, the performance of the WebRT-LiDAR mapping system was evaluated by integrating it into a drone (low-altitude Unmanned Aerial Vehicle) platform. The data received with this system can be stored in databases suitable for the use of artificial intelligence and machine learning–based systems. In this way, the system provides information, predictions, and capabilities that can be used in situations that require instant feedback or decision-making. In this context, the performance of the WebRT-LiDAR system can be further improved and tested in different scenarios, such as disaster management, war strategy, and efficient agriculture in future studies.

In recent years, solar-induced chlorophyll fluorescence (SIF) has been widely studied as an ideal “probe” to directly monitor vegetation photosynthesis. Satellite-retrieved SIF shows great potential for estimating gross primary productivity (GPP), but discontinuous space coverage limits its application. Although there are now some products to reconstruct satellite-scale SIF, it is mainly focused on established carbon satellites such as Orbiting Carbon Observatory-2 (OCO-2) and Tropospheric Monitoring Instrument (TROPOMI). Orbiting Carbon Observatory-3 (OCO-3), a carbon satellite launched by NASA in 2019, has more flexibility and observability than OCO-2, but there are still fewer SIF products for OCO-3. We generated a global contiguous SIF product SIFxco2 (8 days, 0.05 deg) over the period 2000 to 2021 from multivariate OCO-3 data and MODIS. The predictive model is developed by CatBoost. We innovatively added column-averaged atmospheric carbon dioxide concentrations (XCO2) as an explanatory variable, which is usually inversely related to SIF, to improve the model’s SIF explanation performance. SIFxco2 obtains good performance (R2=0.84, RMSE = 0.062) in estimating OCO-3 SIF, and it also correlates well with the flux tower GPP (R2=0.746). In addition, it accurately responds to the temporal and spatial variations of level 2 SIF on a global scale. Our SIFxco2 product holds value in evaluating terrestrial photosynthesis and ecosystem functioning, as well as serving as a benchmark for terrestrial biosphere and Earth system models.

Synthetic aperture radar (SAR) images and optical images have found widespread applications in various remote sensing fields. Although optical images are perceptible to the human visual system, they are susceptible to weather conditions and sunlight illumination. SAR can work all day and in all weather conditions, but SAR images suffer from geometric distortions, shadows, and speckle noise, severely affecting the human eye recognition of SAR images. SAR-to-optical (S2O) image translation can integrate the advantages of two types of images for the practical requirements in remote sensing applications. Existing methods for S2O image translation often produce low-quality translated images with notable deficiencies in color distribution and detail preservation. To address these problems, we propose a domain transfer generative adversarial network (DTGAN) based on U-Net and transformer for S2O image translation. The generator of DTGAN employs U-Net as a framework and incorporates two modules, a spatial transformer module, and a multi-scale channel transformer module. We additionally apply a multi-scale discriminator to train the generator. Extensive experiments are performed with other state-of-the-art methods, and the visual and quantitative evaluation results show that our method not only achieves the optimal metrics but also translates images that are more suitable for observation and analysis.

Single-vehicle light detection and ranging (LiDAR) has limitations in capturing comprehensive environmental information. The advancement of vehicle-to-infrastructure (V2I) collaboration presents a potent solution to this challenge. During the collaboration, point cloud registration precisely aligns data from various LiDARs, effectively mitigating the constraints associated with data collection by a single-vehicle LiDAR. Registration furnishes autonomous vehicles with a more comprehensive and dependable environmental understanding. Currently, there are various types and performances of LiDAR in practical application scenarios. So, it is more necessary to perform heterogeneous point cloud registration, and there is still a relatively large room for improvement. Consequently, we introduce a coarse-to-fine approach to heterogeneous point cloud registration (C2F-HPCR), establishing the inaugural benchmark for point cloud registration in intricate vehicle-infrastructure collaboration contexts. C2F-HPCR acquires an initial registration matrix through its coarse registration module. Subsequently, it uses the overlap estimation module to extract overlap points between two point clouds. These identified points are inputted into the fine registration module to obtain the final registration matrix. Experiments on the DAIR-V2X-C dataset demonstrate that the recall of C2F-HPCR in heterogeneous point cloud registration is 72.99%. C2F-HPCR shows strong performance in heterogeneous point cloud registration, facilitating efficient registration of vehicle-side point clouds and infrastructure-side point clouds. The code is available at https://github.com/916718212/C2F-HPCR

Deep learning-based dehazing of remote sensing images faces two major problems: distortion of remote sensing images and lack of large-scale real paired datasets. To solve these problems, we propose a remote sensing image dehazing method based on data mixing and Laplace network. The Laplace pyramid can divide remote sensing images into different frequency domain layers (the low-frequency layer retains global color information, and the high-frequency layer retains texture details from coarse to fine), and these features are fed into a lightweight multilayer perceptron to learn long-range dependencies. A backbone network consisting of a spatial weighted residual channel attention module can help the residual haze removal module to learn the distribution of haze in remote sensing images for effective haze removal. To address the problem of lack of large-scale real datasets, we cross-mix and restructure the synthetic dataset with the small-sample real dataset, and use the restructured mixed dataset for training, and the trained model can effectively recover the color information of real remote sensing images. After validating the effectiveness and superiority of our model on synthetic datasets, hybrid datasets, and synthetic hyperspectral datasets, we conduct generalizability experiments, and the results show the potential application of our method in advanced vision tasks.

In light of recent advances in deep learning and high-resolution remote sensing imaging technology, there has been a growing adoption of remote sensing ship classification models that are based on deep learning methodologies. However, the efficiency of remote sensing ship classification models is affected by complex backgrounds, shooting conditions, high inter-class similarity of ship targets, and sample diversity. To tackle the challenges above, we propose a multi-scale attention-based adaptive feature fusion (AFF) network for fine-grained ship classification in remote sensing scenarios to improve the fine-grained classification ability of the model from local details. First, using the idea of information complementarity, the multi-scale feature interaction module is constructed in the multi-scale attention module. It employs bidirectional feature interaction paths to concurrently capture intricate details within both deep and shallow ship features, enhancing the interplay among different levels of information. Second, the hybrid attention module is part of the multi-scale attention module. It is designed to enhance the cross-dimensional interaction of spatial domain and channel domain information to amplify the importance of crucial feature regions and feature channels. This allows the network to pay more attention to specific areas and extract distinctive features. Finally, the AFF module is designed to automatically calibrate and fuse different levels of saliency features to obtain features with more fine-grained discrimination for model classification. In this approach, these modules synergistically collaborate and mutually reinforce each other, ultimately increasing the accuracy of ship classification tasks. We evaluated our method on three large-scale fine-grained classification benchmarks; the experimental results show that the proposed method had better fine-grained classification than other methods.

For the land type classification from multi-source remote sensing images with deep learning models, domain adaptation is usually required to improve their generalization ability and work efficiency. We design a dual contrastive learning structure that performs separate contrasts in the encoder and decoder for domain adaptation of the model. Its core utilizes the advantages of contrast learning in classifying unlabeled data. Further optimizations are made toward the loss functions used in the network structure, including a dynamic classification weight adjustment algorithm to treat imbalanced sample numbers and a local dynamic threshold method to filter local noises. With the Potsdam dataset as the source domain and the Vaihingen and LoveDA datasets as the target domains, two sets of experiments are conducted to verify the effectiveness of the model. In addition, corresponding ablation experiments are designed to verify the necessity of the dual contrastive structure. The results show that our method achieved superior performance in building extraction when compared with state-of-the-art methods. Furthermore, it is indicated that the dual contrastive structure is more effective than a single contrastive structure in terms of building extraction.

Seaweed biomass is mainly investigated using quadrat sampling. However, the spatial limitations of quadrat sampling pose significant challenges to the thorough evaluation of seaweed resources at a larger scale. We combined multispectral photography collected using an unmanned aerial vehicle with satellite imagery and in situ hyperspectral reflectance measurement to determine the biomass of Ulva pertusa and Sargassum spp. in the intertidal zone of Gouqi Island, Zhejiang Province, China. The statistical metrics derived from aerial imagery for the biomass estimation of Ulva pertusa and Sargassum spp. achieved coefficients of determination (R2) of 0.81 and 0.74, root mean square errors (RMSE) of 96.54 and 788.64 g/m2, and mean absolute errors (MAE) of 40.71 and 327.12 g/m2, respectively. For satellite imagery, the R2 values were 0.80 and 0.68, RMSE were 97.65 and 910.45 g/m2, and MAE were 71.78 and 719.69 g/m2, respectively. The biomass of U. pertusa and Sargassum spp. fluctuated monthly, reaching their highest values in June and July, respectively. The total biomass of U. pertusa and Sargassum spp. was 1.69×107 and 9.93×107 g, and the organic carbon content was 7.98×105 and 5.04×106 g, respectively. Our research offers a new perspective and methodology, supplying critical data and a scientific rationale for the management of marine assets and the safeguarding of coastal ecologies.

The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides observed flux and cloud products to the climate science community. The CERES instruments, along with the moderate resolution imaging spectroradiometer (MODIS) and visible infrared imaging radiometer suite (VIIRS) imagers, are onboard the Terra, Aqua, NPP, and NOAA20 satellites. To produce seamless multi-platform integrated products, long-term sensor stability and inter-calibration are required. Inter-calibration between sensors within the same Sun-synchronous orbit relies on Earth invariant targets because simultaneous nadir overpasses are not possible. To facilitate inter-calibration efforts, the CERES Imager and Geostationary Calibration Group has improved the Libya-4 target characterization. Improvements include full scan angle characterization to enable daily observations, clear-sky identification using individual scan angle dynamic spatial homogeneity thresholds, and atmospheric corrections. The water vapor correction was found to be effective across the full scan, whereas the ozone and aerosol corrections were less effective. The atmospheric-corrected normalized radiance temporal fluctuations are similar across scan angles and spectral bands and between the MODIS and VIIRS imagers, suggesting that the fluctuations are a result of the natural variability of the Libya-4 surface reflectance. The Libya-4 surface variability is more than likely caused by changes in the prevailing winds that alter sand dune orientation and resulting shadows. The full-scan-imager atmosphere and angle corrected reflected solar band radiance trend standard errors are between 0.6% and 1.0%, and for nearnadir observations, they are between 0.5% and 0.8%. The advanced characterization suggests that the Libya-4 short-term surface reflectance anomalies may need to be considered for imager stability monitoring and inter-calibration efforts.