GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification

Tao, Yiting; Xu, Miaozhong; Zhong, Yanfei; Cheng, Yin‐Jia

doi:10.3390/rs9121328

Cited by 35 publications

(17 citation statements)

References 36 publications

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“…Artificial neural networks (ANNs) are powerful computational models, inspired by the functions of biological neural networks, which were designed to simulate the behavior of the human neural system based on the relationships between input information and the target. ANN is a robust data modeling tool that is widely used in many applications, such as regression, classification, and approximation-based learning processes [ 32 , 33 ]. A common architecture of ANN is divided into three layers, consisting of the input layer, the hidden layer, and the output layer, as illustrated in Figure 3 .…”

Section: Methodsmentioning

confidence: 99%

A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry

Kasantikul

Yang

Wang

et al. 2018

Sensors

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Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with I and Q components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s.

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Section: Methodsmentioning

confidence: 99%

A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry

Kasantikul

Yang

Wang

et al. 2018

Sensors

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“…In some of the applications, the performance of GAN is better than CNN for road detection [30]. Furthermore, StreetGAN for road network synthesis [31] and other GANs [32,33] have been proposed.…”

Section: Related Workmentioning

confidence: 99%

Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery

et al. 2020

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Unmanned aerial vehicle (UAV) remote sensing has a wide area of applications and in this paper, we attempt to address one such problem—road extraction from UAV-captured RGB images. The key challenge here is to solve the road extraction problem using the UAV multiple remote sensing scene datasets that are acquired with different sensors over different locations. We aim to extract the knowledge from a dataset that is available in the literature and apply this extracted knowledge on our dataset. The paper focuses on a novel method which consists of deep TEC (deep transfer learning with ensemble classifier) for road extraction using UAV imagery. The proposed deep TEC performs road extraction on UAV imagery in two stages, namely, deep transfer learning and ensemble classifier. In the first stage, with the help of deep learning methods, namely, the conditional generative adversarial network, the cycle generative adversarial network and the fully convolutional network, the model is pre-trained on the benchmark UAV road extraction dataset that is available in the literature. With this extracted knowledge (based on the pre-trained model) the road regions are then extracted on our UAV acquired images. Finally, for the road classified images, ensemble classification is carried out. In particular, the deep TEC method has an average quality of 71%, which is 10% higher than the next best standard deep learning methods. Deep TEC also shows a higher level of performance measures such as completeness, correctness and F1 score measures. Therefore, the obtained results show that the deep TEC is efficient in extracting road networks in an urban region.

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“…To test the effectiveness and practicability of the proposed method, we introduced five kinds of networks similar to the proposed method: DNN [47], URDNN [48], contextual deep CNN [17], two-stream neural network [26] and SAE + SVM [49]. DNN uses deconvolution to realize an end-to-end, pixel-to-pixel classification.…”

Section: Contrast Experiments With Other Networkmentioning

confidence: 99%

DenseNet-Based Depth-Width Double Reinforced Deep Learning Neural Network for High-Resolution Remote Sensing Image Per-Pixel Classification

Tao

et al. 2018

Remote Sensing

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Abstract:Deep neural networks (DNNs) face many problems in the very high resolution remote sensing (VHRRS) per-pixel classification field. Among the problems is the fact that as the depth of the network increases, gradient disappearance influences classification accuracy and the corresponding increasing number of parameters to be learned increases the possibility of overfitting, especially when only a small amount of VHRRS labeled samples are acquired for training. Further, the hidden layers in DNNs are not transparent enough, which results in extracted features not being sufficiently discriminative and significant amounts of redundancy. This paper proposes a novel depth-width-reinforced DNN that solves these problems to produce better per-pixel classification results in VHRRS. In the proposed method, densely connected neural networks and internal classifiers are combined to build a deeper network and balance the network depth and performance. This strengthens the gradients, decreases negative effects from gradient disappearance as the network depth increases and enhances the transparency of hidden layers, making extracted features more discriminative and reducing the risk of overfitting. In addition, the proposed method uses multi-scale filters to create a wider neural network. The depth of the filters from each scale is controlled to decrease redundancy and the multi-scale filters enable utilization of joint spatio-spectral information and diverse local spatial structure simultaneously. Furthermore, the concept of network in network is applied to better fuse the deeper and wider designs, making the network operate more smoothly. The results of experiments conducted on BJ02, GF02, geoeye and quickbird satellite images verify the efficacy of the proposed method. The proposed method not only achieves competitive classification results but also proves that the network can continue to be robust and perform well even while the amount of labeled training samples is decreasing, which fits the small training samples situation faced by VHRRS per-pixel classification.

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GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification

Cited by 35 publications

References 36 publications

A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry

A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry

Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery

DenseNet-Based Depth-Width Double Reinforced Deep Learning Neural Network for High-Resolution Remote Sensing Image Per-Pixel Classification

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