Filtering of airborne Lidar data by using unsupervised artificial neural networks

Abstract

Spatial data produced with airborne Lidar(Light Detection and Ranging) systems are obtained with high accuracy, fast and low cost. However, manual processing of the data for object extraction is time consuming and labor intensive. Supervised/unsupervised classification methods can be used to make this process automatic. Classification of Lidar data as ground and non-ground data is called filtering. Filtering is very important in creating a Digital Elevation Model using Lidar data. In this study, the discrete-return Lidar test data obtained from the flight at 1200 meters altitude in Bergama district with the Riegl LMS-Q1560 Lidar system produced in 2014 under the chairmanship of the General Directorate of Mapping was used. The Lidar point cloud was grouped into clusters by analyzing it with the Self Organizing Maps (SOM), which is an unsupervised artificial neural network method. Feature classes were determined by comparing clusters with satellite images. The accuracy of the feature classes obtained by this method was calculated by examining all points of the classes which were visually determined. The minimum number of neurons of neural network was determined according to the error values. As a result of filtering the Lidar point cloud with SOM method, Type-1 error was found as 11.54%, Type-2 error was 19.43% and total error was 16.41%. In accordance with the results obtained, it was seen that SOM neural networks with the number of neurons determined could be used effectively in filtering the airborne Lidar data.

Keywords

• Lidar
• Artificial neural networks
• Self organizing maps
• Filtering

Hava Lidar verilerinin denetimsiz yapay sinir ağları kullanılarak filtrelenmesi

Abstract

Hava Lidar (Light Detection and Ranging) sistemleri ile üretilen mekânsal veriler, yüksek doğruluklu, hızlı ve az maliyetli olarak elde edilmektedir. Ancak verilerin nesne çıkarımı amacıyla elle işlenmesi, zaman alan ve emek yoğun bir işlemdir. Bu süreci otomatik bir hale dönüştürmek amacıyla, denetimli/denetimsiz sınıflandırma yöntemleri kullanılabilmektedir. Lidar verilerinin, zemine ait ve zemine ait olmayan veriler olarak ayrılmasına filtreleme denir. Lidar verileri kullanılarak Sayısal Yükseklik Modeli oluşturulmasında filtreleme işlemi büyük önem arz etmektedir. Bu çalışmada, Harita Genel Müdürlüğü’nün başkanlığında 2014 yılında üretilen, Riegl LMS-Q1560 Lidar sistemiyle Bergama ilçesinde 1200 metre yükseklikte gerçekleştirilen uçuş verilerinden elde edilen ayrık-dönüşlü Lidar test verisi kullanılmıştır. Lidar nokta bulutu, denetimsiz bir yapay sinir ağı yöntemi olan Kendini Düzenleyen Haritalar (KDH) yöntemi ile analiz edilerek kümelere ayrılmıştır. Kümeler, uydu görüntüleri ile karşılaştırılarak nesne sınıfları belirlenmiştir. Bu yöntem ile elde edilen nesne sınıflarının doğruluğu, görsel olarak sınıfları belirlenen tüm noktalar incelenerek hesaplanmıştır. Sinir ağına ait en az nöron sayısı, denetimli olarak hata değerlerine göre belirlenmiştir. Lidar nokta bulutunun KDH yöntemiyle filtrelenmesi sonucu, Tip-1 hatası %11.54, Tip-2 hatası %19.43 ve toplam hata %16.41 olarak bulunmuştur. Elde edilen sonuçlara göre, hava Lidar verilerinin filtrelenmesinde KDH sinir ağlarının belirlenen nöron sayısı ile etkin olarak kullanılabildiği görülmüştür.

Keywords

• Lidar
• Yapay sinir ağları
• Kendini düzenleyen haritalar
• Filtreleme

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