Search Results (9)

With the growing demand for ecological monitoring and geological exploration, high-quality satellite remote-sensing imagery has become indispensable for accurate information extraction and automated analysis. However, haze reduces image contrast and sharpness, significantly impairing quality. Existing dehazing methods, primarily designed for natural images, struggle with remote-sensing images due to their complex imaging conditions and scale diversity. Given this, we propose a novel Multi-Scale Contextual Attention Generative Adversarial Network (MCA-GAN), specifically designed for satellite image dehazing. Our method integrates multi-scale feature extraction with global contextual guidance to enhance the network’s comprehension of complex remote-sensing scenes and its sensitivity to fine details. MCA-GAN incorporates two self-designed key modules: (1) a Multi-Scale Feature Aggregation Block, which employs multi-directional global pooling and multi-scale convolutional branches to bolster the model’s ability to capture land-cover details across varying spatial scales; (2) a Dynamic Contextual Attention Block, which uses a gated mechanism to fuse three-dimensional attention weights with contextual cues, thereby preserving global structural and chromatic consistency while retaining intricate local textures. Extensive qualitative and quantitative experiments on public benchmarks demonstrate that MCA-GAN outperforms other existing methods in both visual fidelity and objective metrics, offering a robust and practical solution for remote-sensing image dehazing. Full article

In remote sensing single-image dehazing (RSSID), adjacency effects and the multi-scale characteristics of the land surface–atmosphere system highlight the importance of a network’s effective receptive field (ERF) and its ability to capture multi-scale features. Although multi-scale hybrid models combining convolutional neural networks and Transformers show promise, the quadratic complexity of Transformer complicates the balance between ERF and efficiency. Recently, Mamba achieved global ERF with linear complexity and excelled in modeling long-range dependencies, yet its design for sequential data and channel redundancy limits its direct applicability to RSSID. To overcome these challenges and improve performance in RSSID, we present a novel Mamba-based dehazing network, U-shaped Dual Attention Vision Mamba Network (UDAVM-Net) for Satellite RSSID, which integrates multi-path scanning and incorporates dual attention mechanisms to better capture non-uniform haze features while reducing redundancy. The core module, Residual Vision Mamba Blocks (RVMBs), are stacked within a U-Net architecture to enhance multi-scale feature learning. Furthermore, to enhance the model’s applicability to real-world remote sensing data, we abandoned overly simplified haze image degradation models commonly used in existing works, instead adopting an atmospheric radiative transfer model combined with a cloud distortion model to construct a submeter-resolution satellite RSSID dataset. Experimental results demonstrate that UDAVM-Net consistently outperforms competing methods on the StateHaze1K dataset, our newly proposed dataset, and real-world remote sensing images, underscoring its effectiveness in diverse scenarios. Full article

Satellites frequently encounter atmospheric haze during imaging, leading to the loss of detailed information in remote sensing images and significantly compromising image quality. This detailed information is crucial for applications such as Earth observation and environmental monitoring. In response to the above issues, this paper proposes an end-to-end multi-scale adaptive feature extraction method for remote sensing image dehazing (MSD-Net). In our network model, we introduce a dilated convolution adaptive module to extract global and local detail features of remote sensing images. The design of this module can extract important image features at different scales. By expanding convolution, the receptive field is expanded to capture broader contextual information, thereby obtaining a more global feature representation. At the same time, a self-adaptive attention mechanism is also used, allowing the module to automatically adjust the size of its receptive field based on image content. In this way, important features suitable for different scales can be flexibly extracted to better adapt to the changes in details in remote sensing images. To fully utilize the features at different scales, we also adopted feature fusion technology. By fusing features from different scales and integrating information from different scales, more accurate and rich feature representations can be obtained. This process aids in retrieving lost detailed information from remote sensing images, thereby enhancing the overall image quality. A large number of experiments were conducted on the HRRSD and RICE datasets, and the results showed that our proposed method can better restore the original details and texture information of remote sensing images in the field of dehazing and is superior to current state-of-the-art methods. Full article

Haze or cloud always shrouds satellite images, obscuring valuable geographic information for military surveillance, natural calamity surveillance and mineral resource exploration. Satellite image dehazing (SID) provides the possibility for better applications of satellite images. Most of the existing dehazing methods are tailored for natural images and are not very effective for satellite images with non-homogeneous haze since the semantic structure information and inconsistent attenuation are not fully considered. To tackle this problem, this study proposes a hierarchical semantic-guided contextual structure-aware network (SCSNet) for spectral satellite image dehazing. Specifically, a hybrid CNN–Transformer architecture integrated with a hierarchical semantic guidance (HSG) module is presented to learn semantic structure information by synergetically complementing local representation from non-local features. Furthermore, a cross-layer fusion (CLF) module is specially designed to replace the traditional skip connection during the feature decoding stage so as to reinforce the attention to the spatial regions and feature channels with more serious attenuation. The results on the SateHaze1k, RS-Haze, and RSID datasets demonstrated that the proposed SCSNet can achieve effective dehazing and outperforms existing state-of-the-art methods. Full article

Haze and mist caused by air quality, weather, and other factors can reduce the clarity and contrast of images captured by cameras, which limits the applications of automatic driving, satellite remote sensing, traffic monitoring, etc. Therefore, the study of image dehazing is of great significance. Most existing unsupervised image-dehazing algorithms rely on a priori knowledge and simplified atmospheric scattering models, but the physical causes of haze in the real world are complex, resulting in inaccurate atmospheric scattering models that affect the dehazing effect. Unsupervised generative adversarial networks can be used for image-dehazing algorithm research; however, due to the information inequality between haze and haze-free images, the existing bi-directional mapping domain translation model often used in unsupervised generative adversarial networks is not suitable for image-dehazing tasks, and it also does not make good use of extracted features, which results in distortion, loss of image details, and poor retention of image features in the haze-free images. To address these problems, this paper proposes an end-to-end one-sided unsupervised image-dehazing network based on a generative adversarial network that directly learns the mapping between haze and haze-free images. The proposed feature-fusion module and multi-scale skip connection based on residual network consider the loss of feature information caused by convolution operation and the fusion of different scale features, and achieve adaptive fusion between low-level features and high-level features, to better preserve the features of the original image. Meanwhile, multiple loss functions are used to train the network, where the adversarial loss ensures that the network generates more realistic images and the contrastive loss ensures a meaningful one-sided mapping from the haze image to the haze-free image, resulting in haze-free images with good quantitative metrics and visual effects. The experiments demonstrate that, compared with existing dehazing algorithms, our method achieved better quantitative metrics and better visual effects on both synthetic haze image datasets and real-world haze image datasets. Full article

Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated. Full article

With increasing advancement of science and technology, remote sensing satellite imaging does not only monitor the Earth’s surface environment instantly and accurately but also helps to prevent destruction from inevitable disasters. The changing weather, e.g., cloudiness or haze formed from atmospheric suspended particles, results in low contrast satellite images, and partial information about Earth’s surface is lost. Therefore, this study proposes an effective dehazing method for one single satellite image, aiming to enhance the image contrast and filter out the region covered with haze, so as to reveal the lost information. First, the initial transmission map of the image is estimated using an Interval Type-2 Recurrent Fuzzy Cerebellar Model Articulation Controller (IT2RFCMAC) model. For the halo and color oversaturation resulted from the processing procedure, a bilateral filter and quadratic function nonlinear conversion are used in turn to refine the initial transmission map. For the estimation of atmospheric light, the first 1% brightest region is used as the color vector of atmospheric light. Finally, the refined transmission map and atmospheric light are used as the parameters for reconstructing the image. The experimental results show that the proposed satellite image dehazing method has good performance in the visibility detail and color contrast of the reconstructed image. In order to further validate the effectiveness of the proposed method, visual assessment and quantitative evaluation were implemented, respectively, and compared with the methods proposed by relevant scholars. The visual assessment and quantitative evaluation analysis demonstrated good results of the proposed approach. Full article

Whenever vegetated areas are monitored over time, phenological changes in land cover should be decoupled from changes in acquisition conditions, like atmospheric components, Sun and satellite heights and imaging instrument. This especially holds when the multispectral (MS) bands are sharpened for spatial resolution enhancement by means of a panchromatic (Pan) image of higher resolution, a process referred to as pansharpening. In this paper, we provide evidence that pansharpening of visible/near-infrared (VNIR) bands takes advantage of a correction of the path radiance term introduced by the atmosphere, during the fusion process. This holds whenever the fusion mechanism emulates the radiative transfer model ruling the acquisition of the Earth’s surface from space, that is for methods exploiting a multiplicative, or contrast-based, injection model of spatial details extracted from the panchromatic (Pan) image into the interpolated multispectral (MS) bands. The path radiance should be estimated and subtracted from each band before the product by Pan is accomplished. Both empirical and model-based estimation techniques of MS path radiances are compared within the framework of optimized algorithms. Simulations carried out on two GeoEye-1 observations of the same agricultural landscape on different dates highlight that the de-hazing of MS before fusion is beneficial to an accurate detection of seasonal changes in the scene, as measured by the normalized differential vegetation index (NDVI). Full article

Optical satellite imagery is often contaminated by the persistent presence of clouds and atmospheric haze. Without an effective method for removing this contamination, most optical remote sensing applications are less reliable. In this research, a methodology has been developed to fully automate and improve the Haze Optimized Transformation (HOT)-based haze removal. The method is referred to as AutoHOT and characterized with three notable features: a fully automated HOT process, a novel HOT image post-processing tool and a class-based HOT radiometric adjustment method. The performances of AutoHOT in haze detection and compensation were evaluated through three experiments with one Landsat-5 TM, one Landsat-7 ETM+ and eight Landsat-8 OLI scenes that encompass diverse landscapes and atmospheric haze conditions. The first experiment confirms that AutoHOT is robust and effective for haze detection. The average overall, user’s and producer’s accuracies of AutoHOT in haze detection can reach 96.4%, 97.6% and 97.5%, respectively. The second and third experiments demonstrate that AutoHOT can not only accurately characterize the haze intensities but also improve dehazed results, especially for brighter targets, compared to traditional HOT radiometric adjustment. Full article