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An Improved Transfer-Learning for Image-Based species classification of Protected Indonesians Birds

Computers, Materials & Continua 2022年第12期73卷 4577-4593页

作者： Chao-Lung Yang Yulius Harjoseputro Yu-Chen Hu Yung-Yao Chen Department Industrial Management National Taiwan University of Science and TechnologyTaipei106Taiwan Department Electronics and Computer Engineering National Taiwan University of Science and TechnologyTaipei106Taiwan Department Informatics Universitas Atma Jaya YogyakartaYogyakarta55281Indonesia Department Computer Science and Information Management Providence UniversityTaichung433Taiwan

This research proposed an improved transfer-learning bird classification framework to achieve a more precise classification of Protected Indonesia Birds(PIB)which have been identified as the endangered bird *** framework takes advantage of using the proposed sequence of Batch Normalization Dropout Fully-Connected(BNDFC)layers to enhance the baseline model of transfer *** main contribution of this work is the proposed sequence of BNDFC that can be applied to any Convolutional Neural Network(CNN)based model to improve the classification accuracy,especially for image-based species classification *** experiment results show that the proposed sequence of BNDFC layers outperform other combination of *** addition of BNDFC can improve the model’s performance across ten different CNN-based *** average,BNDFC can improve by approximately 19.88%in Accuracy,24.43%in F-measure,17.93%in G-mean,23.41%in Sensitivity,and 18.76%in ***,applying fine-tuning(FT)is able to enhance the accuracy by 0.85%with a smaller validation loss of 18.33%*** addition,MobileNetV2 was observed to be the best baseline model with the lightest size of 35.9 MB and the highest accuracy of 88.07%in the validation set.

关键词： Transfer learning convolutional neural network(CNN) species classification protected indonesia bird(PIB)

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Unmanned aerial vehicles(UAV)for assessment of qualitative classification of Norway spruce in temperate forest stands

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Geo-Spatial Information Science 2018年第1期21卷 12-20页

作者： Olga Brovkina Emil Cienciala Peter Surový Přemysl Janata Global Change Research Institute(CzechGlobe) CASBrnoCzech Republic Department of Ecosystem Analyses Institute of Forest Ecosystem Research(IFER)Jilove u PrahyCzech Republic Department of Forest Management Czech University of Life SciencesPragueCzech Republic Department of Forest Management and Applied Geoinformatics Mendel University in BrnoBrnoCzech Republic

The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest *** objectives are:(1)to test the applicability of UAV visible an near-infrared(VNIR)and geometrical data based on Z values of point dense cloud(PDC)raster to separate forest species and dead trees in the study area;(2)to explore the relationship between UAV VNIR data and individual spruce health indicators from field sampling;and(3)to explore the possibility of the qualitative classification of spruce health *** based on NDVI and PDC raster was successfully applied for separation of spruce and silver fir,and for identification of dead tree *** between common beech and fir was distinguished by the object-oriented image *** was able to identify the presence of key indicators of spruce health,such as mechanical damage on stems and stem resin exudation linked to honey fungus infestation,while stem damage by peeling was identified at the significance *** results contributed to improving separation of coniferous(spruce and fir)tree species based on VNIR and PDC raster UAV data,and newly demonstrated the potential of NDVI for qualitative classification of spruce *** proposed methodology can be applicable for monitoring of spruce health condition in the local forest sites.

关键词： Remote sensing species classification spruce health indicator Unmanned Aerial System(UAS)

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