Comparison of Pixel-based and Object-based classification for glacier change detection

This research paper compares the result of Object based and Pixel based classification techniques for glacier change detection on Landsat Thematic mapper (TM) and Enhanced Thematic Mapper (ETM+) imageries. The objective of this study is to see which classification method performs better for change detection in mountainous regions. Northern face of Himalayan region, The study area is undergoing climate change in the form of rapid melting of glacial ice mass, expansion of the existing lakes and creation of new lakes. This results in Glacial Lake Outburst Flood (GLOF) and breach or outburst from ice and ‘moraine dams’ causing devastating floods downstream. The global warming phenomenon worldwide has resulted in a significant decrease in glacial cover. Glacier's change monitoring and permafrost-related hazards have long been studied using remote sensing data and techniques to assess the damage. The world is facing a serious problem of handling the climate change issue and its effects on humans as well as on natural resources. Glaciers are considered as one of the best indicators of climate change [1]. Landsat TM/ETM+ images were used for glacier change monitoring of Turkey's mountains project, Mount Suphan. The results show that about ¾ of total area of suphan glacier has been lost in 23 years. Traditional image classification methods use only the spectral information at pixel level without considering the shape of underlying objects [2]. However, object-based image classification process uses spectral and spatial dimensions (shape of feature) in order to perform classification. In this study, multi temporal Landsat TM and ETM+ image from 1990 to 2010 have been used. Initially, the traditional pixel-based classification was performed on Landsat thematic layers and layers developed from indices like NDVI and NDSII. Then object-based classification of these images was carried out. The comparison of the classification results (both qualitative and quantitative) show that the object-based approach gives about 10–15% higher accuracy, much better results in terms of area estimation and change detection of snow covered areas as compared to traditional pixel-based classification. The results also indicate that object based classification is more useful in mountainous regions to avoid confusion among classes produced by shadows.