scholarly journals Remote Sensing Image Augmentation Based on Text Description for Waterside Change Detection

2021 ◽  
Vol 13 (10) ◽  
pp. 1894
Author(s):  
Chen Chen ◽  
Hongxiang Ma ◽  
Guorun Yao ◽  
Ning Lv ◽  
Hua Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Change Detection ◽  
Data Augmentation ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Administrative Procedure ◽  
Adversarial Network ◽  
Training Samples ◽  
Perlin Noise

Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, “Inception Score”, “Human Rank”, and “Inference Time” are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model.

scholarly journals Super-Resolution for Hyperspectral Remote Sensing Images Based on the 3D Attention-SRGAN Network

2020 ◽  
Vol 12 (7) ◽  
pp. 1204
Author(s):  
Xinyu Dou ◽  
Chenyu Li ◽  
Qian Shi ◽  
Mengxi Liu
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Super Resolution ◽  
Hyperspectral Remote Sensing ◽  
Spectral Information ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Hyperspectral Remote Sensing Images ◽  
3D Attention

Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying and analyzing targets in HSIs. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. To better restore the spectral information in the HSI SR field, a novel super-resolution (SR) method was proposed in this study. Firstly, we innovatively used three-dimensional (3D) convolution based on SRGAN (Super-Resolution Generative Adversarial Network) structure to not only exploit the spatial features but also preserve spectral properties in the process of SR. Moreover, we used the attention mechanism to deal with the multiply features from the 3D convolution layers, and we enhanced the output of our model by improving the content of the generator’s loss function. The experimental results indicate that the 3DASRGAN (3D Attention-based Super-Resolution Generative Adversarial Network) is both visually quantitatively better than the comparison methods, which proves that the 3DASRGAN model can reconstruct high-resolution HSIs with high efficiency.

scholarly journals WSGAN: An Improved Generative Adversarial Network for Remote Sensing Image Road Network Extraction by Weakly Supervised Processing

2021 ◽  
Vol 13 (13) ◽  
pp. 2506
Author(s):  
Anna Hu ◽  
Siqiong Chen ◽  
Liang Wu ◽  
Zhong Xie ◽  
Qinjun Qiu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Road Network ◽  
Road Networks ◽  
Google Earth ◽  
Binary Image ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Weakly Supervised

Road networks play an important role in navigation and city planning. However, current methods mainly adopt the supervised strategy that needs paired remote sensing images and segmentation images. These data requirements are difficult to achieve. The pair segmentation images are not easy to prepare. Thus, to alleviate the burden of acquiring large quantities of training images, this study designed an improved generative adversarial network to extract road networks through a weakly supervised process named WSGAN. The proposed method is divided into two steps: generating the mapping image and post-processing the binary image. During the generation of the mapping image, unlike other road extraction methods, this method overcomes the limitations of manually annotated segmentation images and uses mapping images that can be easily obtained from public data sets. The residual network block and Wasserstein generative adversarial network with gradient penalty loss were used in the mapping network to improve the retention of high-frequency information. In the binary image post-processing, this study used the dilation and erosion method to remove salt-and-pepper noise and obtain more accurate results. By comparing the generated road network results, the Intersection over Union scores reached 0.84, the detection accuracy of this method reached 97.83%, the precision reached 92.00%, and the recall rate reached 91.67%. The experiments used a public dataset from Google Earth screenshots. Benefiting from the powerful prediction ability of GAN, the experiments show that the proposed method performs well at extracting road networks from remote sensing images, even if the roads are covered by the shadows of buildings or trees.

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 Unsupervised Change Detection Using Fast Fuzzy Clustering for Landslide Mapping from Very High-Resolution Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1381 ◽  
Author(s):  
Tao Lei ◽  
Dinghua Xue ◽  
Zhiyong Lv ◽  
Shuying Li ◽  
Yanning Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Change Detection ◽  
Level Set ◽  
Execution Time ◽  
Remote Sensing Images ◽  
Fuzzy C Means ◽  
Landslide Mapping ◽  
Very High

Change detection approaches based on image segmentation are often used for landslide mapping (LM) from very high-resolution (VHR) remote sensing images. However, these approaches usually have two limitations. One is that they are sensitive to thresholds used for image segmentation and require too many parameters. The other one is that the computational complexity of these approaches depends on the image size, and thus they require a long execution time for very high-resolution (VHR) remote sensing images. In this paper, an unsupervised change detection using fast fuzzy c-means clustering (CDFFCM) for LM is proposed. The proposed CDFFCM has two contributions. The first is that we employ a Gaussian pyramid-based fast fuzzy c-means (FCM) clustering algorithm to obtain candidate landslide regions that have a better visual effect due to the utilization of image spatial information. The second is that we use the difference of image structure information instead of grayscale difference to obtain more accurate landslide regions. Three comparative approaches, edge-based level-set (ELSE), region-based level-set (RLSE), and change detection-based Markov random field (CDMRF), and the proposed CDFFCM are evaluated in three true landslide cases in the Lantau area of Hong Kong. The experiments show that the proposed CDFFCM is superior to three comparative approaches in terms of higher accuracy, fewer parameters, and shorter execution time.

scholarly journals A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

2021 ◽  
Author(s):  
Lulu Tian ◽  
Zidong Wang ◽  
Weibo Liu ◽  
Yuhua Cheng ◽  
Fuad E. Alsaadi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Thermal Imaging ◽  
Crack Detection ◽  
Data Augmentation ◽  
Thermal Environment ◽  
Generative Adversarial Network ◽  
Penalty Term ◽  
Adversarial Network ◽  
Imaging Test

AbstractAs a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset.

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