Genetic Algorithm Based Tree Segmentation

Varjovi, Mahdi Hatami; Altun, Sara; Talu, Muhammed Fatih; Yeroğlu, Celaleddin

doi:10.1109/idap.2018.8620822

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Genetic Algorithm1

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2020

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“…Every new generation which survives in arrays created by random information exchange is obtained through combining. These are population, reproduction, discard, modification, and evaluation [19,33,34]. The building of the Genetic Algorithm contains some genetic operations as shown in Figure 4.…”

Section: Genetic Algorithmmentioning

confidence: 99%

UC-Merced Image Classification with CNN Feature Reduction Using Wavelet Entropy Optimized with Genetic Algorithm

Özyurt¹,

Avcı²,

Sert³

2020

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The classification of high-resolution and remote sensed terrain images with high accuracy is one of the greatest challenges in machine learning. In the present study, a novel CNN feature reduction using Wavelet Entropy Optimized with Genetic Algorithm (GA-WEE-CNN) method was used for remote sensing images classification. The optimal wavelet family and optimal value of the parameters of the Wavelet Sure Entropy (WSE), Wavelet Norm Entropy (WNE), and Wavelet Threshold Entropy (WTE) were calculated, and given to classifiers such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The efficiency of the proposed hybrid method was tested using the UC-Merced dataset. 80% of the data were used as training data, and a performance rate of 98.8% was achieved with SVM classifier, which has been the highest ratio compared to all studies using same dataset so far with only 18 features. These results proved the advantage of the proposed method.

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Section: Genetic Algorithmmentioning

confidence: 99%