2019
DOI: 10.4236/jcc.2019.77006
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Semantic Segmentation Based Remote Sensing Data Fusion on Crops Detection

Abstract: Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusi… Show more

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Cited by 13 publications
(10 citation statements)
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“…In the model, batch normalisation (BN) layers were applied after all convolution layers to have higher learning rates. Using the BN helps reduce overfitting by improving the capacity of generalisation of the CNN architecture and increasing the speed of its learning process by accelerating the convergence (Pena et al, 2019). The learning rate of this work is featured a gradual reduction starting from 0.001 dropping down to learning rate of 0.0006, which resulted in an acceptable performance within cross-validation.…”
Section: Convolution Neural Network (Cnn)mentioning
confidence: 97%
See 1 more Smart Citation
“…In the model, batch normalisation (BN) layers were applied after all convolution layers to have higher learning rates. Using the BN helps reduce overfitting by improving the capacity of generalisation of the CNN architecture and increasing the speed of its learning process by accelerating the convergence (Pena et al, 2019). The learning rate of this work is featured a gradual reduction starting from 0.001 dropping down to learning rate of 0.0006, which resulted in an acceptable performance within cross-validation.…”
Section: Convolution Neural Network (Cnn)mentioning
confidence: 97%
“…CNNs have led in the state-of-the-art feature extraction results and are considered a hot topic in the image processing and computer vision fields, which gradually overcome traditional methods (Pena et al, 2019). CNNs as kind of mature network of deep learning model is inspired from the biological multi-layer neural networks architectures, which enable them to form high-level semantic features from the existing low-level features in an image (Jin et al, 2019).…”
Section: Convolution Neural Network (Cnn)mentioning
confidence: 99%
“…For this reason, many detection and segmentation networks employ techniques to fuse these views to combine the useful features of each. Multi-view satellite fusion methods have been successfully utilized for tasks including crop segmentation [22], target detection [18,19], and a whole host of other application areas [8,23,20].…”
Section: Related Workmentioning
confidence: 99%
“…The straightforward approach towards such a challenging setting is to pretrain a network with the view that is missing at test time, and subsequently fine-tune the network using data available both during training and testingan approach commonly employed in satellite imagery analysis [28,10,29,1]. At the same time, several approaches have been proposed on multi-view learning for satellite imagery for a variety of tasks [22,18,19]. However, these methods are not tailored for handling missing modalities at test-time, and are thus unsuitable for the specific setting under consideration.…”
Section: Introductionmentioning
confidence: 99%
“…The advantage of data fusion of data received from multiple different sensors relies in "an improved estimate of physical phenomenon via redundant observations" [14]. The efficiency of data fusion was previously demonstrated in precision agriculture domain [15][16][17][18]. In [15], authors present the benefits on crop monitoring of 2D and 3D data fusion for a vineyard monitoring and use the results in order to classify vines in serveral classes by processing data from multiple sources (different sensors, Unmanned Aerial Vehicle (UAVs), etc).…”
Section: Data Fusion For Agricultural Areamentioning
confidence: 99%