Thematic mapping of urban areas from WorldView-2 satellite imagery using machine learning algorithms

Abstract

Thematic mapping of urban areas from WorldView-2 satellite imagery using machine learning algorithmsMonitoring and mapping urban areas from remote sensing imagery using image classification techniques are important for urban and regional planners and municipalities. High resolution satellite images are essential data sources for the generation of urban land-use/land-cover maps. In this paper, urban thematic maps are generated from WorldView-2 satellite images using machine learning algorithms, namely random forest and support vector machines classifiers. The performances of these classifiers are evaluated and compared using four test areas that have different urban characteristics. The obtained visual and quantitative results for four test areas indicate the effectiveness of the machine learning algorithms in urban thematic mapping. The overall accuracies were computed in the range of 89.92 and 96.38 and overall kappa values were computed in the range of 0.8790 and 0.9566 for random forest classifier, which are considerably high. Similarly, for support vector machines classifier, the overall accuracies were computed in the range of 91.98 and 96.07 and overall kappa values were computed in the range of 0.9038 and 0.9528. The results also show that different classification accuracies for different test areas are related to the properties of the selected urban patterns

Keywords

• Urban thematic mapping,Random forest classifier,Support vector machines classifier,WorldView-2,Antalya

Kentsel Alanların WorldView-2 uydu görüntülerinden makine öğrenme algoritmaları kullanılarak tematik haritalanması

Abstract

Kentsel alanların uzaktan algılama görüntülerinden görüntü sınıflandırma teknikleri kullanılarak izlenmesi ve haritalanması şehir ve bölge plancıları ve belediyeler için önemlidir. Yüksek çözünürlüklü uydu görüntüleri arazi-kullanımı/arazi-örtüsü haritalarının elde edilmesinde önemli veri kaynaklarıdır. Bu makalede, makine öğrenme algoritmalarından rastgele orman ve destek vektör makineleri sınıflandırmaları kullanılarak WorldView-2 uydu görüntülerinden kentsel tematik haritalar elde edilmiştir. Bu sınıflandırmaların performansları seçilen farklı kentsel karakteristiklere sahip dört test alanında değerlendirilmiş ve karşılaştırılmıştır. Dört test alanı için elde edilen görsel ve nicel sonuçlar, makine öğrenme algoritmalarının kentsel tematik haritalamada verimliliğini göstermektedir. Rastgele orman sınıflandırması kullanıldığında sınıflandırma doğrulukları 89.92 ile 96.38 değerleri arasında ve kappa değerleri 0.8790 ile 0.9566 değerleri arasında hesaplanmıştır ki bu değerler oldukça yüksektir. Benzer şekilde, destek vektör makineleri sınıflandırması kullanıldığında sınıflandırma doğrulukları 91.98 ile 96.07 değerleri arasında ve kappa değerleri 0.9038 ile 0.9528 değerleri arasında hesaplanmıştır. Sonuçlar, ayrıca farklı test alanları için elde edilen farklı sınıflandırma doğruluklarının seçilen kentsel dokuların özellikleriyle ilişkili olduğunu göstermektedir

Keywords

• Kentsel tematik haritalama,Rastgele orman sınıflandırması,Destek vektör makineleri sınıflandırması,WorldView-2,Antalya

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