scholarly journals Edge Loss for Remote Sensing Image Super-Resolution

2021 ◽  
Author(s):  
Jiaoyue Li ◽  
Weifeng Liu ◽  
Kai Zhang ◽  
Baodi Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Super Resolution ◽  
Ground Truth ◽  
Remote Sensing Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Sensing Applications ◽  
Image Super Resolution ◽  
Edge Based

Remote sensing image super-resolution (SR) plays an essential role in many remote sensing applications. Recently, remote sensing image super-resolution methods based on deep learning have shown remarkable performance. However, directly utilizing the deep learning methods becomes helpless to recover the remote sensing images with a large number of complex objectives or scene. So we propose an edge-based dense connection generative adversarial network (SREDGAN), which minimizes the edge differences between the generated image and its corresponding ground truth. Experimental results on NWPU-VHR-10 and UCAS-AOD datasets demonstrate that our method improves 1.92 and 0.045 in PSNR and SSIM compared with SRGAN, respectively.

scholarly journals VEHICLE DETECTION IN REMOTE SENSING IMAGES USING DEEP NEURAL NETWORKS AND MULTI-TASK LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 797-804
Author(s):  
M. Cao ◽  
H. Ji ◽  
Z. Gao ◽  
T. Mei
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Learning Community ◽  
Feature Fusion ◽  
Super Resolution ◽  
Vehicle Detection ◽  
Remote Sensing Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Benchmark Datasets

Abstract. Vehicle detection in remote sensing image has been attracting remarkable attention over past years for its applications in traffic, security, military, and surveillance fields. Due to the stunning success of deep learning techniques in object detection community, we consider to utilize CNNs for vehicle detection task in remote sensing image. Specifically, we take advantage of deep residual network, multi-scale feature fusion, hard example mining and homography augmentation to realize vehicle detection, which almost integrates all the advanced techniques in deep learning community. Furthermore, we simultaneously address super-resolution (SR) and detection problems of low-resolution (LR) image in an end-to-end manner. In consideration of the absence of paired low-/highresolution data which are generally time-consuming and cumbersome to collect, we leverage generative adversarial network (GAN) for unsupervised SR. Detection loss is back-propagated to SR generator to boost detection performance. We conduct experiments on representative benchmark datasets and demonstrate that our model yields significant improvements over state-of-the-art methods in deep learning and remote sensing areas.

scholarly journals TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution

2021 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Fayaz Ali Dharejo ◽  
Farah Deeba ◽  
Yuanchun Zhou ◽  
Bhagwan Das ◽  
Munsif Ali Jatoi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Single Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Image Super Resolution ◽  
Spatio Temporal ◽  
Single Image Super Resolution

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR) . However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256 × 256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers.

scholarly journals Unpaired Remote Sensing Image Super-Resolution with Multi-Stage Aggregation Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 3167
Author(s):  
Lize Zhang ◽  
Wen Lu ◽  
Yuanfei Huang ◽  
Xiaopeng Sun ◽  
Hongyi Zhang
Keyword(s):  
Remote Sensing ◽  
Super Resolution ◽  
Perceptual Content ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Imaging Device ◽  
Adversarial Network ◽  
Multi Stage ◽  
Image Super Resolution

Mainstream image super-resolution (SR) methods are generally based on paired training samples. As the high-resolution (HR) remote sensing images are difficult to collect with a limited imaging device, most of the existing remote sensing super-resolution methods try to down-sample the collected original images to generate an auxiliary low-resolution (LR) image and form a paired pseudo HR-LR dataset for training. However, the distribution of the generated LR images is generally inconsistent with the real images due to the limitation of remote sensing imaging devices. In this paper, we propose a perceptually unpaired super-resolution method by constructing a multi-stage aggregation network (MSAN). The optimization of the network depends on consistency losses. In particular, the first phase is to preserve the contents of the super-resolved results, by constraining the content consistency between the down-scaled SR results and the low-quality low-resolution inputs. The second stage minimizes perceptual feature loss between the current result and LR input to constrain perceptual-content consistency. The final phase employs the generative adversarial network (GAN) to adding photo-realistic textures by constraining perceptual-distribution consistency. Numerous experiments on synthetic remote sensing datasets and real remote sensing images show that our method obtains more plausible results than other SR methods quantitatively and qualitatively. The PSNR of our network is 0.06dB higher than the SOTA method—HAN on the UC Merced test set with complex degradation.

scholarly journals Target Detection Method for Low-Resolution Remote Sensing Image Based on ESRGAN and ReDet

Photonics ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 431
Author(s):  
Yuwu Wang ◽  
Guobing Sun ◽  
Shengwei Guo
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Detection Method ◽  
Recognition Rate ◽  
Super Resolution ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Adversarial Network

With the widespread use of remote sensing images, low-resolution target detection in remote sensing images has become a hot research topic in the field of computer vision. In this paper, we propose a Target Detection on Super-Resolution Reconstruction (TDoSR) method to solve the problem of low target recognition rates in low-resolution remote sensing images under foggy conditions. The TDoSR method uses the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to perform defogging and super-resolution reconstruction of foggy low-resolution remote sensing images. In the target detection part, the Rotation Equivariant Detector (ReDet) algorithm, which has a higher recognition rate at this stage, is used to identify and classify various types of targets. While a large number of experiments have been carried out on the remote sensing image dataset DOTA-v1.5, the results of this paper suggest that the proposed method achieves good results in the target detection of low-resolution foggy remote sensing images. The principal result of this paper demonstrates that the recognition rate of the TDoSR method increases by roughly 20% when compared with low-resolution foggy remote sensing images.

scholarly journals Super-Resolution of Remote Sensing Images via a Dense Residual Generative Adversarial Network

2019 ◽  
Vol 11 (21) ◽  
pp. 2578 ◽  
Author(s):  
Wen Ma ◽  
Zongxu Pan ◽  
Feng Yuan ◽  
Bin Lei
Keyword(s):  
Remote Sensing ◽  
Network Architecture ◽  
Super Resolution ◽  
Objective Evaluation ◽  
Generative Adversarial Networks ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Memory Mechanism ◽  
Adversarial Network ◽  
Sensing Applications

Single image super-resolution (SISR) has been widely studied in recent years as a crucial technique for remote sensing applications. In this paper, a dense residual generative adversarial network (DRGAN)-based SISR method is proposed to promote the resolution of remote sensing images. Different from previous super-resolution (SR) approaches based on generative adversarial networks (GANs), the novelty of our method mainly lies in the following factors. First, we made a breakthrough in terms of network architecture to improve performance. We designed a dense residual network as the generative network in GAN, which can make full use of the hierarchical features from low-resolution (LR) images. We also introduced a contiguous memory mechanism into the network to take advantage of the dense residual block. Second, we modified the loss function and altered the model of the discriminative network according to the Wasserstein GAN with a gradient penalty (WGAN-GP) for stable training. Extensive experiments were performed using the NWPU-RESISC45 dataset, and the results demonstrated that the proposed method outperforms state-of-the-art methods in terms of both objective evaluation and subjective perspective.

scholarly journals Devnagari Handwritten Characters Image Super-Resolution based on Enhanced SRGAN

2021 ◽  
Vol 16 (1) ◽  
pp. 103-109
Author(s):  
Prasiddha Siwakoti ◽  
Sharad Kumar Ghimire
Keyword(s):  
Super Resolution ◽  
Image Data ◽  
Ground Truth ◽  
Frequency Component ◽  
High Frequency Component ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Batch Normalization ◽  
Statistical Similarity ◽  
Image Super Resolution

The difficulty in machine learning-based image super-resolution is to generate high-frequency component in an image without introducing any artifacts. In this paper, Devnagari handwritten characters image using a generative adversarial network with a classifier is generated in high-resolution which is also classifiable. The generator architecture is modified by removing all batch normalization layers in generator architecture with a residual in residual dense block. Batch normalization is removed because it produces unwanted artifacts in the generated images. A Devnagari handwritten characters classifier is built using CNN. The classifier is used in the network to calculate the content loss. The adversarial loss is obtained from the GAN architecture and both of the losses are added to obtain total loss. Generated HR images is validated using six different evaluation metrics among which MSE, PSNR determines pixel-wise difference and SSIM compares images perceptually. Similarly, FID is used to measure the statistical similarity between the batch of generated images and its original batch. Finally, the Gradient similarity is used to assess the quality of the generated image. From the experimental results, we obtain MSE, PSNR and SSIM as 0.0507, 12.95(dB) and 0.8172 respectively. Similarly, the FID value obtained was 27.5 with the classification accuracy of image data of 98%. The gradient similarity between the generated image and the ground truth obtained was 0.9124.

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