3 research outputs found
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
Convolutional Neural Networks (CNNs) have been consistently proved
state-of-the-art results in image Super-Resolution (SR), representing an
exceptional opportunity for the remote sensing field to extract further
information and knowledge from captured data. However, most of the works
published in the literature have been focusing on the Single-Image
Super-Resolution problem so far. At present, satellite based remote sensing
platforms offer huge data availability with high temporal resolution and low
spatial resolution. In this context, the presented research proposes a novel
residual attention model (RAMS) that efficiently tackles the multi-image
super-resolution task, simultaneously exploiting spatial and temporal
correlations to combine multiple images. We introduce the mechanism of visual
feature attention with 3D convolutions in order to obtain an aware data fusion
and information extraction of the multiple low-resolution images, transcending
limitations of the local region of convolutional operations. Moreover, having
multiple inputs with the same scene, our representation learning network makes
extensive use of nestled residual connections to let flow redundant
low-frequency signals and focus the computation on more important
high-frequency components. Extensive experimentation and evaluations against
other available solutions, either for single or multi-image super-resolution,
have demonstrated that the proposed deep learning-based solution can be
considered state-of-the-art for Multi-Image Super-Resolution for remote sensing
applications
Deep Residual Squeeze and Excitation Network for Remote Sensing Image Super-Resolution
Recently, deep convolutional neural networks (DCNN) have obtained promising results in single image super-resolution (SISR) of remote sensing images. Due to the high complexity of remote sensing image distribution, most of the existing methods are not good enough for remote sensing image super-resolution. Enhancing the representation ability of the network is one of the critical factors to improve remote sensing image super-resolution performance. To address this problem, we propose a new SISR algorithm called a Deep Residual Squeeze and Excitation Network (DRSEN). Specifically, we propose a residual squeeze and excitation block (RSEB) as a building block in DRSEN. The RSEB fuses the input and its internal features of current block, and models the interdependencies and relationships between channels to enhance the representation power. At the same time, we improve the up-sampling module and the global residual pathway in the network to reduce the parameters of the network. Experiments on two public remote sensing datasets (UC Merced and NWPU-RESISC45) show that our DRSEN achieves better accuracy and visual improvements against most state-of-the-art methods. The DRSEN is beneficial for the progress in the remote sensing images super-resolution field