Integration of pixel-based and object-based image classification for extraction of water bodies with Landsat imagery
College of Liberal Arts and Sciences
Social and Behavioral Sciences
We propose two new methods for fast extraction of water features in remotely sensed imagery. Our first method is a pixel-based procedure that utilizes indices and band values. Based on their characteristic spectral reflectance curves, water bodies are grouped into three types - clear, green, and turbid. We found that the MNDWI is best suited for identifying clear water. Green water has its maximum reflectance occurring in Landsat Thematic Mapper (TM) band 4 (NIR band), while turbid water has its maximum reflectance in TM band 5 (mid-infrared band). Our second method integrates our pixel-based classification with object-based image segmentation. Two Landsat scenes in Shaanxi Province, China were used as the primary data source. Digital elevation models (DEMs) and their derived slope maps were used as ancillary information. To evaluate the performance of the proposed methods, extraction results of the three existing methods and our two new methods were compared and assessed.
2013 Annual Meeting The Association of American Geographers (AAG)
Los Angeles, CA
Sun, Wanxiao; Sun, Fangdi; Gong, Peng; and Chen, Jin, "Integration of pixel-based and object-based image classification for extraction of water bodies with Landsat imagery" (2013). Faculty Scholarly Dissemination Grants. 1040.