Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion
84
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Deep learning techniques have made an increasing impact on the field of remote sensing. However, deep neural networks based fusion of multimodal data from different remote sensors with heterogenous characteristics has not been fully explored, due to the lack of availability of big amounts of perfectly aligned multi-sensor image data with diverse scenes of high resolutions, especially for synthetic aperture radar (SAR) data and optical imagery. To promote the development of deep learning based SAR-optical fusion approaches, we release the QXS-SAROPT dataset, which contains 20,000 pairs of SAR-optical image patches. We obtain the SAR patches from SAR satellite GaoFen-3 images and the optical patches from Google Earth images. These images cover three port cities: San Diego, Shanghai and Qingdao. Here, we present a detailed introduction of the construction of the dataset, and show its two representative exemplary applications, namely SAR-optical image matching and SAR ship detection boosted by cross-modal information from optical images. As a large open SAR-optical dataset with multiple scenes of a high resolution, we believe QXS-SAROPT will be of potential value for further research in SAR-optical data fusion technology based on deep learning.
rate research
Read More
The data fusion technology aims to aggregate the characteristics of different data and obtain products with multiple data advantages. To solves the problem of reduced resolution of PolSAR images due to system limitations, we propose a fully polarimetric synthetic aperture radar (PolSAR) images and single-polarization synthetic aperture radar SAR (SinSAR) images fusion network to generate high-resolution PolSAR (HR-PolSAR) images. To take advantage of the polarimetric information of the low-resolution PolSAR (LR-PolSAR) image and the spatial information of the high-resolution single-polarization SAR (HR-SinSAR) image, we propose a fusion framework for joint LR-PolSAR image and HR-SinSAR image and design a cross-attention mechanism to extract features from the joint input data. Besides, based on the physical imaging mechanism, we designed the PolSAR polarimetric loss function for constrained network training. The experimental results confirm the superiority of fusion network over traditional algorithms. The average PSNR is increased by more than 3.6db, and the average MAE is reduced to less than 0.07. Experiments on polarimetric decomposition and polarimetric signature show that it maintains polarimetric information well.
The effective combination of the complementary information provided by the huge amount of unlabeled multi-sensor data (e.g., Synthetic Aperture Radar (SAR), optical images) is a critical topic in remote sensing. Recently, contrastive learning methods have reached remarkable success in obtaining meaningful feature representations from multi-view data. However, these methods only focus on the image-level features, which may not satisfy the requirement for dense prediction tasks such as the land-cover mapping. In this work, we propose a new self-supervised approach to SAR-optical data fusion that can learn disentangled pixel-wise feature representations directly by taking advantage of both multi-view contrastive loss and the bootstrap your own latent (BYOL) methods. Two key contributions of the proposed approach are a multi-view contrastive loss to encode the multimodal images and a shift operation to reconstruct learned representations for each pixel by building the local consistency between different augmented views. In the experimental period, we first verified the effectiveness of multi-view contrastive loss and BYOL in self-supervised learning on SAR-optical fusion using an image-level classification task. Then we validated the proposed approach on a land-cover mapping task by training it with unlabeled SAR-optical image pairs. There we used labeled data pairs to evaluate the discriminative capability of learned features in downstream tasks. Results show that the proposed approach extracts features that result in higher accuracy and that reduces the dimension of representations with respect to the image-level contrastive learning method.
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise. Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120-degree cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method.
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, outperforming traditional non-learning approaches in speed and accuracy. However, many of the current deep learning approaches are not data consistent, require in vivo training data or do not solve all steps of the QSM processing pipeline. Here we aim to overcome these limitations and developed a framework to solve the QSM processing steps jointly. We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational network that combines the QSM model term and a learned regularizer. We demonstrate that NeXtQSM overcomes the limitations of previous model-agnostic deep learning methods and show that NeXtQSM offers a complete deep learning based pipeline for computing robust, fast and accurate quantitative susceptibility maps.
Public satellite missions are commonly bound to a trade-off between spatial and temporal resolution as no single sensor provides fine-grained acquisitions with frequent coverage. This hinders their potential to assist vegetation monitoring or humanitarian actions, which require detecting rapid and detailed terrestrial surface changes. In this work, we probe the potential of deep generative models to produce high-resolution optical imagery by fusing products with different spatial and temporal characteristics. We introduce a dataset of co-registered Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat surface reflectance time series and demonstrate the ability of our generative model to blend coarse daily reflectance information into low-paced finer acquisitions. We benchmark our proposed model against state-of-the-art reflectance fusion algorithms.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
