Shadows significantly reduce the available information in remote sensing images, obstructing downstream tasks such as object detection, scene classification, and localization. However, shadow removal from remote sensing images is still an open issue, for the following reasons. Firstly, deep neural networks are difficult to train since the corresponding ground truths of shadows are almost always unavailable in practice. Secondly, the existing shadow removal methods still suffer from blurry details and boundary artifacts. In this paper, we describe how a generative shadow synthesis and removal framework that couples data-driven methods with illumination models was developed to address the above challenges effectively. Various shadows were synthesized in shadow-free regions of remote sensing images by GSS-Net, which is a generative shadow synthesis network that considers the physical process of shadow illumination attenuation. In this way, a large-scale, diverse, and realistic shadow dataset (RS-SynShadow) was built. A generative shadow removal network—GSR-Net—embedding a histogram-enhanced illumination model, was then developed for high-fidelity shadow removal without artifacts. Extensive experiments conducted on synthetic and real data demonstrate that the proposed shadow synthesis and removal framework significantly outperforms the state-of-the-art methods, both visually and quantitatively. The dataset and code will be made available at https://github.com/fzzfRS/RS-GSSR.

Shadow removal is beneficial for various remote sensing applications, such as semantic segmentation and object detection. However, traditional shadow removal methods perform poorly when applied to high-resolution remote sensing images. Some deep learning-based models exhibit high-level accuracy of removal, but it is difficult to be expanded to multi-scenarios due to insufficient pairs of shadow/shadow-free data in reality. In this paper, a novel framework is proposed from shadow synthesis to shadow removal, aiming to improve the usability of deep learning-based models in the task of shadow removal in high-resolution remote sensing images. First, we combine the physical shadow illumination model and a domain alignment network to synthesize a large variety of realistic shadows. Then, a shadow removal network considering global-local features is built to restore the ground surface details finely. Numerous experiments have shown that the proposed framework can effectively remove various shadows and is superior to existing methods.

There is no "cloudy & cloud-free" paired images with totally identical surface information under the same spatial and temporal condition in reality, which limits the development of supervised deep learning methods in the field of cloud removal and detection. In this regard, a high-fidelity thin cloud synthetic method is proposed, which is more challenging than the synthesis of thick clouds. This method combines physical model and data-driven methods. The expression of scattering laws at the pixel level is extended to the channel level to synthesize multi-channel cloud from cirrus band with controlled cloud thickness. Besides, spatial and spectral features are learned from real data using generative adversarial networks and transformed to synthetic data. Based on it, a dataset containing infinite number of "cloudy & cloud-free" pairs can be constructed. Experimental results show that the proposed method has the best visual effect with highest quantitative evaluations compared with current methods.

Transportation network extraction based on deep learning has become a hotspot. However, the existing models all aim to distinguish between background and transportation network, while ignoring the class attributes within the transportation networks. In this letter, we propose a multi-class transportation network extraction network (MCTN-Net) to simultaneously extract railways, roadways, trails and bridges. Inspired by multi-task learning, the network first extracts the semantic and information together by the use of a dense feature shared encoder (DFSE). The orientation and semantic features are then fused in the orientation-guided stacking module (OGSM) to enhance the connection between transportation network pixels. Furthermore, a semantic refinement branch (SRB) is designed to improve the ability of classifying different transportation network types through deep supervised fusion and class attention. A multi-class transportation network dataset was constructed and used in the experiments. The experiential results indicate that the proposed method achieves an MIoU of 64.29% and an FWIoU of 71.20% without the background, which is significantly better than the other road extraction models and semantic segmentation methods. The code and dataset are available at https://github.com/fzzfRS/MCTN-Net.

Shadow correction is an important task in the analysis of high-resolution remote sensing images, as the existence of shadows reduces radiometric information and causes changes in the energy distribution. This is especially the case when the spatial resolution is very high, as the shadows disturb the subsequent processing and applications, such as image mosaicking, classification, segmentation, etc. Traditional shadow correction methods are limited by the shadow detection accuracy and the available non-shaded samples in the imagery. In this paper, we propose an evolutionary shadow correction network (ESCNet) and describe how we built a benchmark unmanned aerial vehicle (UAV) image dataset to achieve shadow correction directly, without shadow detection. The proposed ESCNet is made up of two sub-networks with an evolutionary relationship: a shadow removal network (SRNet) followed by a radiation adjustment network (RANet). The shadows are first removed by SRNet trained on the UAV image dataset to achieve the primary shadow-corrected image, and the global radiation is then adjusted to a sunlit-like status by RANet. Shadow detection is not required in the proposed method, which effectively overcomes the error accumulation and shadow edge artifact problem of the traditional methods. Experiments were carried out and the results were compared with those of both traditional and deep learning-based shadow correction methods, for which both qualitative and quantitative evaluations were performed. The results suggest that the proposed method shows obvious advantages in information recovery for shadow regions, and the global brightness of the corrected imagery is consistent with that of sunlit conditions.