Improved Urban Flooding Mapping from Remote Sensing Images Using Generalized Regression Neural Network-Based Super-Resolution Algorithm

Abstract Hyperspectral imaging is a versatile and powerful technology for gathering geo-data. Planes and satellites equipped with hyperspectral cameras are currently the leading contenders for large-scale imaging projects. Aiming at the shortcomings of traditional methods for detecting sparse representation of multi-spectral images, this paper proposes wireless sensor networks (WSNs) based single-hyperspectral image super-resolution method based on deep residual convolutional neural networks. We propose a different strategy that involves merging cheaper multispectral sensors to achieve hyperspectral-like spectral resolution while maintaining the WSN's spatial resolution. This method studies and mines the nonlinear relationship between low-resolution remote sensing images and high-resolution remote sensing images, constructs a deep residual convolutional neural network, connects multiple residual blocks in series, and removes some unnecessary modules. For this purpose, a decision support system is used that provides the outcome to the next layer. Finally, this paper, fully explores the similarities between natural images and hyperspectral images, use natural image samples to train convolutional neural networks, and further use migration learning to introduce the trained network model to the super-resolution problem of high-resolution remote sensing images, and solve the lack of training samples problem. A comparison between different algorithms for processing data on datasets collected in situ and via remote sensing is used to evaluate the proposed approach. The experimental results show that the method has good performance and can obtain better super-resolution effects.

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Improved Sparse Representation Super-Resolution algorithm for Remote Sensing Image

MATEC Web of Conferences ◽

10.1051/matecconf/201823202040 ◽

2018 ◽

Vol 232 ◽

pp. 02040

Author(s):

Fuzhen Zhu ◽

Xin Huang ◽

Yue Liu ◽

Haitao Zhu

Keyword(s):

Remote Sensing ◽

Sparse Representation ◽

Super Resolution ◽

Objective Evaluation ◽

Remote Sensing Images ◽

Low Resolution ◽

Resolution Image ◽

Training Images ◽

Feature Extract ◽

Resolution Algorithm

In order to obtain higher quality super-resolution reconstruction (SRR) of remote sensing images, an improved sparse representation remote sensing images SRR method is proposed in this paper. First, low-resolution image is processed by improved feature extract operator. The high-resolution image and low-resolution image blocks have the same sparse representation coefficient, so the SRR image with higher spatial resolution can be derived from the sparse representation coefficients which have been obtained from low-resolution image. The improved feature extraction operator is a method to get more detail and texture information from the training images. Experiment results show that more texture details can be obtained in the result of SRR remote sensing images subjectively. At the same time, the objective evaluation parameters are improved greatly. The peak PSNR is increased about 2.50dB and 0.50 dB, RMSE is decreased about 2.80 and 0.3 compared with bicubic interpolation algorithm and Ref[8] algorithm respectively.

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Super-resolution reconstruction of remote sensing images based on convolutional neural network

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.13.4.046502 ◽

2019 ◽

Vol 13 (04) ◽

pp. 1 ◽

Cited By ~ 3

Author(s):

Yu Tian ◽

Rui-Sheng Jia ◽

Shao-Hua Xu ◽

Rong Hua ◽

Meng-Di Deng

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Super Resolution ◽

Remote Sensing Images

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Narrow Road Extraction from Remote Sensing Images Based on Super-Resolution Convolutional Neural Network

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8517851 ◽

2018 ◽

Author(s):

Xinyu Zhou ◽

Xi Chen ◽

Ye Zhang

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Super Resolution ◽

Road Extraction ◽

Remote Sensing Images ◽

Narrow Road

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Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural Network

Forests ◽

10.3390/f10090778 ◽

2019 ◽

Vol 10 (9) ◽

pp. 778 ◽

Cited By ~ 5

Author(s):

Zhou ◽

Wu ◽

Zhou ◽

Fang ◽

Zheng ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Basal Area ◽

Tree Age ◽

Generalized Regression Neural Network ◽

Independent Variables ◽

Modeling And Prediction ◽

Independent Variable ◽

Generalized Regression ◽

Better Than

The diameter at breast height (DBH) is an important factor used to estimate important forestry indices like forest growing stock, basal area, biomass, and carbon stock. The traditional DBH ground surveys are time-consuming, labor-intensive, and expensive. To reduce the traditional ground surveys, this study focused on the prediction of unknown DBH in forest stands using existing measured data. As a comparison, the tree age was first used as the only independent variable in establishing 13 kinds of empirical models to fit the relationship between the age and DBH of the forest subcompartments and predict DBH growth. Second, the initial independent variables were extended to 19 parameters, including 8 ecological and biological factors and 11 remote sensing factors. By introducing the Spearman correlation analysis, the independent variable parameters were dimension-reduced to satisfy very significant conditions (p ≤ 0.01) and a relatively large correlation coefficient (r ≥ 0.1). Finally, the remaining independent variables were involved in the modeling and prediction of DBH using a multivariate linear regression (MLR) model and generalized regression neural network (GRNN) model. The (root-mean-squared errors) RMSEs of MLR and GRNN were 1.9976 cm and 1.9655 cm, respectively, and the R2 were 0.6459 and 0.6574 respectively, which were much better than the values for the 13 traditional empirical age–DBH models. The use of comprehensive factors is beneficial to improving the prediction accuracy of both the MLR and GRNN models. Regardless of whether remote sensing image factors were included, the experimental results produced by GRNN were better than MLR. By synthetically introducing ecological, biological, and remote sensing factors, GRNN produced the best results with 1.4688 cm in mean absolute error (MAE), 13.78% in MAPE, 1.9655 cm for the RMSE, 0.6574 for the R2, and 0.0810 for the Theil’s inequality coefficient (TIC), respectively. For modeling and prediction based on more complex tree species and a wider range of samples, GRNN is a desirable model with strong generalizability.

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Lightweight Feedback Convolution Neural Network for Remote Sensing Images Super-Resolution

IEEE Access ◽

10.1109/access.2021.3052946 ◽

2021 ◽

Vol 9 ◽

pp. 15992-16003

Author(s):

Jin Wang ◽

Yiming Wu ◽

Liu Wang ◽

Lei Wang ◽

Osama Alfarraj ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Super Resolution ◽

Convolution Neural Network ◽

Remote Sensing Images

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Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network

Remote Sensing ◽

10.3390/rs11131588 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1588 ◽

Cited By ~ 13

Author(s):

Tao Lu ◽

Jiaming Wang ◽

Yanduo Zhang ◽

Zhongyuan Wang ◽

Junjun Jiang

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Frequency ◽

Satellite Images ◽

Satellite Image ◽

Super Resolution ◽

Object Identification ◽

Remote Sensing Images ◽

Frequency Information ◽

Multi Scale

Recently, the application of satellite remote sensing images is becoming increasingly popular, but the observed images from satellite sensors are frequently in low-resolution (LR). Thus, they cannot fully meet the requirements of object identification and analysis. To utilize the multi-scale characteristics of objects fully in remote sensing images, this paper presents a multi-scale residual neural network (MRNN). MRNN adopts the multi-scale nature of satellite images to reconstruct high-frequency information accurately for super-resolution (SR) satellite imagery. Different sizes of patches from LR satellite images are initially extracted to fit different scale of objects. Large-, middle-, and small-scale deep residual neural networks are designed to simulate differently sized receptive fields for acquiring relative global, contextual, and local information for prior representation. Then, a fusion network is used to refine different scales of information. MRNN fuses the complementary high-frequency information from differently scaled networks to reconstruct the desired high-resolution satellite object image, which is in line with human visual experience (“look in multi-scale to see better”). Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities.

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Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network

Remote Sensing ◽

10.3390/rs11151815 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1815 ◽

Cited By ~ 7

Author(s):

Jia ◽

Ge ◽

Chen ◽

Li ◽

Heuvelink ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Land Cover ◽

Convolutional Neural Network ◽

Super Resolution ◽

Fine Scale ◽

Remote Sensing Images ◽

Resolution Mapping ◽

Super Resolution Mapping ◽

Land Cover Maps

Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN (SRMCNN) is proposed and tested. Specifically, an encoder-decoder CNN is used to model the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. Two real-image experiments were conducted to analyze the effectiveness of the proposed method. The results demonstrate that the overall accuracy of the proposed SRMCNN method was 3% to 5% higher than that of two existing SRM methods. Moreover, the proposed SRMCNN method was validated by visualizing output features and analyzing the performance of different geographic objects.

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