Author_Institution :
State Key Lab. of Inf. Eng. in Surveying, Mapping & Remote Sensing, Wuhan Univ., Wuhan, China
Abstract :
Water is one of the vital components for the ecological environment, which plays an important role in human survival and socioeconomic development. Water resources in urban areas are gradually decreasing due to the rapid urbanization, especially in developing countries. Therefore, the precise extraction and automatic identification of water bodies are of great significance and urgently required for urban planning. It should be noted that although some studies have been reported regarding the water-area extraction, to our knowledge, few papers concern the identification of urban water types (e.g, rivers, lakes, canals, and ponds). In this paper, a novel two-level machine-learning framework is proposed for identifying the water types from urban high-resolution remote-sensing images. The framework consists of two interpretation levels: 1) water bodies are extracted at the pixel level, where the water/shadow/vegetation indexes are considered and 2) water types are further identified at the object level, where a set of geometrical and textural features are used. Both levels employ machine learning for the image interpretation. The proposed framework is validated using the GeoEye-1 and WorldView-2 images, over two mega cities in China, i.e, Wuhan and Shenzhen, respectively. The experimental results show that the proposed method achieved satisfactory accuracies for both water extraction [95.4% (Shenzhen), 96.2% (Wuhan)], and water type classification [94.1% (Shenzhen), 95.9% (Wuhan)] in complex urban areas.
Keywords :
geophysical image processing; hydrological techniques; image classification; image resolution; learning (artificial intelligence); water resources; China; GeoEye-1 images; Shenzhen; WorldView-2 images; Wuhan; automatic water-body type identification; canals; ecological environment; geometrical features; high-resolution remote-sensing imagery; human survival development; image interpretation; lakes; object-based machine learning; pixel-based machine learning; ponds; rivers; shadow index; socioeconomic development; textural features; two-level machine-learning framework; urban areas; urban high-resolution remote-sensing images; urban planning; urbanization; vegetation index; water index; water resources; water type classification; water-area extraction; Feature extraction; Indexes; Lakes; Remote sensing; Rivers; Vegetation mapping; Water resources; High resolution; machine learning; object-oriented; water detection; water extraction; water index;