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

IEEE Access ◽  
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

scholarly journals A Neural Network-Based Distributed Compressed Sensing Algorithm to Derive Continuous Hyperspectral Images in Wireless Sensor Networks

2022 ◽  
Author(s):  
Md. Sarkar Hasanuzzaman
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
High Resolution ◽  
Super Resolution ◽  
Hyperspectral Images ◽  
Wireless Sensor ◽  
Remote Sensing Images

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.

A saliency model-oriented convolution neural network for cloud detection in remote sensing images

2021 ◽  
Vol 17 (3) ◽  
pp. 235-247
Author(s):  
Jun Zhang ◽  
Junjun Liu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Environmental Changes ◽  
Convolution Neural Network ◽  
Kernel Principal Component Analysis ◽  
Optical Remote Sensing ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Saliency Model

Remote sensing is an indispensable technical way for monitoring earth resources and environmental changes. However, optical remote sensing images often contain a large number of cloud, especially in tropical rain forest areas, make it difficult to obtain completely cloud-free remote sensing images. Therefore, accurate cloud detection is of great research value for optical remote sensing applications. In this paper, we propose a saliency model-oriented convolution neural network for cloud detection in remote sensing images. Firstly, we adopt Kernel Principal Component Analysis (KCPA) to unsupervised pre-training the network. Secondly, small labeled samples are used to fine-tune the network structure. And, remote sensing images are performed with super-pixel approach before cloud detection to eliminate the irrelevant backgrounds and non-clouds object. Thirdly, the image blocks are input into the trained convolutional neural network (CNN) for cloud detection. Meanwhile, the segmented image will be recovered. Fourth, we fuse the detected result with the saliency map of raw image to further improve the accuracy of detection result. Experiments show that the proposed method can accurately detect cloud. Compared to other state-of-the-art cloud detection method, the new method has better robustness.

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

2019 ◽  
Vol 13 (04) ◽  
pp. 1 ◽  
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

scholarly journals Remote Sensing Image Ship Detection under Complex Sea Conditions Based on Deep Semantic Segmentation

2020 ◽  
Vol 12 (4) ◽  
pp. 625 ◽  
Author(s):  
Yantong Chen ◽  
Yuyang Li ◽  
Junsheng Wang ◽  
Weinan Chen ◽  
Xianzhong Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Random Field ◽  
Conditional Random Field ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Deep Convolution Neural Network ◽  
End To End

Under complex sea conditions, ship detection from remote sensing images is easily affected by sea clutter, thin clouds, and islands, resulting in unreliable detection results. In this paper, an end-to-end convolution neural network method is introduced that combines a deep convolution neural network with a fully connected conditional random field. Based on the Resnet architecture, the remote sensing image is roughly segmented using a deep convolution neural network as the input. Using the Gaussian pairwise potential method and mean field approximation theorem, a conditional random field is established as the output of the recurrent neural network, thus achieving end-to-end connection. We compared the proposed method with other state-of-the-art methods on the dataset established by Google Earth and NWPU-RESISC45. Experiments show that the target detection accuracy of the proposed method and the ability of capturing fine details of images are improved. The mean intersection over union is 83.2% compared with other models, which indicates obvious advantages. The proposed method is fast enough to meet the needs for ship detection in remote sensing images.

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

2019 ◽  
Vol 11 (13) ◽  
pp. 1588 ◽  
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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