Classification of the tree for aerial image using a deep convolution neural network and visual feature clustering

Lin, Chuen Horng; Yu, Chia Ching; Wang, Ting You; Chen, Tsung Yi

doi:10.1007/s11227-019-03012-3

Cited by 9 publications

(5 citation statements)

References 39 publications

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“…As shown in Figure 4 , it is the structure of the translation quality estimation model, and its algorithm content is as follows: the two sentence vectors obtained by bidirectional recurrent neural network are expressed as ctx x and ctx y , and these two expressions are used to generate new vectors, respectively, V x , V y as follows [ 14 , 15 ]:

…”

Section: Establishment and Optimization Of Machine Online Translation Model Based On Deep Convolution Neural Network Algorithmmentioning

confidence: 99%

[Retracted] Optimization of Machine Online Translation System Based on Deep Convolution Neural Network Algorithm

Zhao

2021

Computational Intelligence and Neuroscience

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In order to effectively optimize the machine online translation system and improve its translation efficiency and translation quality, this study uses the deep separable convolution neural network algorithm to construct a machine online translation model and evaluates the quality on the basis of pseudo data learning. In order to verify the performance of the model, the regression performance experiment of the model, the method performance experiment of generating pseudo data for specific tasks, the sorting task performance experiment of the model, and the machine translation quality comparison experiment are designed. RMSE and MAE were used to evaluate the regression task performance of the model. Spearman rank correlation coefficient and delta AVG value were used to evaluate the sorting task performance of the model. The experimental results show that the MAE and RMSE values of the model are decreased by 2.28% and 1.39%, respectively, compared with the baseline system under the same experimental conditions, and the Spearman and delta AVG values are increased by 132% and 100.7%, respectively, compared with the baseline system. The method of generating pseudo data for specific tasks needs less data and can make the translation system reach a better level faster. When the number of instances is more than 10, the quality score of the model output is higher than that of Google translation whose similarity is more than 0.8.

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…”

Section: Establishment and Optimization Of Machine Online Translation Model Based On Deep Convolution Neural Network Algorithmmentioning

confidence: 99%

[Retracted] Optimization of Machine Online Translation System Based on Deep Convolution Neural Network Algorithm

Zhao

2021

Computational Intelligence and Neuroscience

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“…Additionally, the importance of data quality in the performance of models and Artificial Intelligence engines must be considered. Lin et al [98] used transfer learning from a deep network called You Only Look Once (YOLO) version 3. They achieved only an accuracy of 49%, even though YOLO is a deep, robust, and fast NN [130].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…This type of image can become an approximation to the human visual perception of the urban space, as it reflects its complexity from a panoramic perspective [45,104]. Recent studies demonstrated the potential of DGI to characterize objects, including trees, in urban lands [91,98]. The most popular sources of DGI include Google Street View (GSV) [52] images and Tencent [53] images (for China).…”

Section: Ground-level Images and Videosmentioning

confidence: 99%

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Remotely Sensed Tree Characterization in Urban Areas: A Review

et al. 2021

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Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented.

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“…Zhang et al [ 1 ] created a fruit classification system using a deep neural network to replace handcrafted features, beating state-of-the-art approaches. Horng et al [ 2 ] used a DNN, feeding it with aerial images to classify tree areas and help in the understanding of land use. Sebti et al [ 3 ] provided a solution for forecasting and diagnosing of diabetic retinopathy by training a convolutional neural network with retina images, achieving over 96% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification

Torres

González

Martínez

2022

Sensors

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Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination of fuzzy image edge-detection and the usage of a convolutional neural network for a computer vision system to classify guitar types according to their body model. The focus of this investigation is to compare the effects of performing image-preprocessing techniques on raw data (non-normalized images) with different fuzzy edge-detection methods, specifically fuzzy Sobel, fuzzy Prewitt, and fuzzy morphological gradient, before feeding the images into a convolutional neural network to perform a classification task. We propose and compare two convolutional neural network architectures to solve the task. Fuzzy edge-detection techniques are compared against their classical counterparts (Sobel, Prewitt, and morphological gradient edge-detection) and with grayscale and color images in the RGB color space. The fuzzy preprocessing methodologies highlight the most essential features of each image, achieving favorable results when compared to the classical preprocessing methodologies and against a pre-trained model with both proposed models, as well as achieving a reduction in training times of more than 20% compared to RGB images.

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Classification of the tree for aerial image using a deep convolution neural network and visual feature clustering

Cited by 9 publications

References 39 publications

[Retracted] Optimization of Machine Online Translation System Based on Deep Convolution Neural Network Algorithm

[Retracted] Optimization of Machine Online Translation System Based on Deep Convolution Neural Network Algorithm

Remotely Sensed Tree Characterization in Urban Areas: A Review

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification

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