Derin Öğrenme Yaklaşımlarıyla Çevresel İzlemeye Yönelik Çok-Sınıflı Sınıflandırma

Year 2022, , 307 - 314, 30.11.2022

Şaziye Özge Atik

https://doi.org/10.31590/ejosat.1057643

Abstract

Sınıflandırma haritaları, çevresel izleme görevlerinin ana çıktı türlerinden biridir. Bu çalışmada, görüntü sınıflandırması için uzaktan algılama verileri kullanılarak derin öğrenme algoritmaları uygulanmıştır. Uygulamada UC Merced ve WHU-RS19 olmak üzere iki veri seti üzerinde farklı CNN modelleri kullanılmıştır. Test aşamasında derin öğrenme modellerinin tahminleri ile çok-sınıflı sınıflandırma yapılmış ve sınıflandırmaya ait değerlendirme ölçütleri hesaplanmıştır. Kullanılan CNN modellerinin veri setlerindeki performansları genel doğruluk ölçütünde değerlendirilmiştir. DenseNet201 modelinin, UC Merced ve WHU-RS19 veri setlerinin her ikisinde de testlerde daha yüksek performanslı sonuçlara sahip olduğu gözlemlenmiştir. Elde edilen sonuçlar, literatürdeki diğer çalışmaların sonuçlarıyla karşılaştırılmıştır. UC Merced veri setindeki uygulamada %98.81 genel doğruluk ile bu çalışmada kullanılan DenseNet201 modelinin diğer çalışmalardan daha yüksek performansa sahip olduğu gözlenmiştir. Ayrıca, her iki veri setinde benzer olan arazi kullanım sınıfları belirlenmiş ve en iyi performans gösteren algoritmadaki sonuçları yorumlanmıştır, Benzer sınıfların yapılan testlerde sınıflandırılması kesinlik, duyarlılık ve F1 skoru ölçütleri kullanılarak değerlendirilmiştir.

Keywords

Derin öğrenme, Çevresel izleme, Evrişimsel Sinir Ağları, Çok-sınıflı sınıflandırma

Multi-Class Classification for Environmental Monitoring with Deep Learning Approaches

Abstract

Classification maps are one of the main output types of environmental monitoring tasks. In this study, deep learning algorithms were applied using remote sensing data for image classification. In the application, different CNN models were used on two data sets, UC Merced and WHU-RS19. In the test phase, multi-class classification was made with the predictions of deep learning models and the evaluation criteria of the classification were calculated. The performances of the CNN models used in the data sets were evaluated in the overall accuracy metric. It has been observed that the DenseNet201 model has higher performance results in tests on both the UC Merced and WHU-RS19 datasets. The results obtained were compared with the results of other studies in the literature. It has been observed that the DenseNet201 model used in this study has higher performance than other studies with an overall accuracy of 98.81% in the application in the UC Merced dataset. In addition, land use classes that are similar in both data sets were determined and the results of the best performing algorithm were interpreted. Classification of similar classes in the tests was evaluated using the evaluation metrics of precision, recall and F1 score.

Keywords

Deep learning, Environmental Monitoring, Convolutional Neural Networks; Multi-class classification

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