Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images

Yang, Ming‐Der; Tseng, Hsin-Hung; Hsu, Yu‐Chun; Tsai, Hui Ping

doi:10.3390/rs12040633

Cited by 133 publications

(76 citation statements)

References 28 publications

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“…In the future, the proposed VGI setup can be promoted through the execution of street-monitoring techniques to supply long-term continuous imagery; these images, acquired by smartphone cameras, can then be incorporated into Google Street View to construct and update local spatial information. Eventually, by employing more deep learning techniques with distribution computation [63], a great amount of VGI photos can be processed and integrated into a disaster-monitoring system for flooding and traffic management in an economical and time-efficient manner.…”

Section: Discussionmentioning

confidence: 99%

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

et al. 2020

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Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities.

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Section: Discussionmentioning

confidence: 99%

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

et al. 2020

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“…Yang et al [8] explored two neural network-based deep learning algorithms to segment color images collected using an unmanned aerial vehicle (UAV) for identifying lodged rice fields. The methodology and results described in their paper indicated that deep learning-based image segmentation (classification) outperformed the conventional Maximum Likelihood method, not only with a higher accuracy but also being 10 to 15 times faster.…”

Section: Machine Learning For Close-range Photogrammetry and Image Anmentioning

confidence: 99%

Editorial for the Special Issue on Selected Papers from the “2019 International Symposium on Remote Sensing”

et al. 2020

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The 2019 International Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications with geospatial sensors, systems, and data, in conjunction with emerging technologies such as artificial intelligence, machine leaning and advanced spatial analysis algorithms. The topics of the selected papers include the following: the on-orbit radiometric calibration of satellite optical sensors, environmental characteristics assessment with remote sensing, machine learning-based photogrammetry and image analysis, and the integration of remote sensing and spatial analysis. The selected contributions also demonstrate and discuss various sophisticated applications in utilizing remote sensing, geospatial data, and technologies to address different environmental and societal issues. Readers should find the Special Issue enlightening and insightful for understanding state-of-the-art remote sensing and spatial information science research, development and applications.

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“…Currently, several attempts have been made to use Deep Learning Neural Networks (DLNN) for the classification of herbs [86][87][88][89][90][91][92][93][94][95][96][97][98][99], and plants diseases [100][101][102][103][104][105][106][107]. The motivation is to attract attention to such methods after the state-of-the-art processing of natural images [84,85].…”

Section: Introductionmentioning

confidence: 99%

Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning

Muneer

Fati

2020

IEEE Access

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Recognizing the desired herb among thousands of herbs is an exhausting and time-consuming practice. Hence, herbs identification via a vision system is beneficial since the pharmacist and botanic need not to collect them through traditional ways. Thus, this paper proposed an efficient and automatic classification system to recognize Malaysian herbs that would be used in medical or cooking areas. As per the authors' knowledge, there is no evidence for similar studies on medical herbs in Malaysia. In the proposed system, we have investigated different classifiers to build an efficient classifier; then, the classifier was integrated with a mobile app to ease the real-time classification. The proposed system employed two classifiers, namely Support Vector Machine (SVM) and Deep Learning Neural Network (DLNN). The two models have been tested on our own dataset, which contains 1000 leaves. The experimental results showed that SVM achieved 74.63% recognition accuracy and DLNN achieved 93% recognition accuracy for both of the experimental model and the developed mobile app. Furthermore, the processing time was 4 seconds for SVM and 5 seconds for DLNN classifier, while the processing time using the mobile app was 2 seconds only.

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Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images

Cited by 133 publications

References 28 publications

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

Editorial for the Special Issue on Selected Papers from the “2019 International Symposium on Remote Sensing”

Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning

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