Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography

By C. Cleve, Maggi Kelly, F. Kearns, M. Moritz. Computers, Environment and Urban Systems, 2008

298 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/ec22e6f54b6f5b78c868295a514e8af29a9140fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Jiantao Liu
2. Jianhua Gong
3. Quanlong Feng
4. Xiaoxiao Li
5. Guofan Shao

In Global and Planetary Change

1. Xiaoxiao Li
2. Jiantao Liu
3. Jianhua Gong
4. Guofan Shao
5. Quanlong Feng

In Health, Toxicology and Mutagenesis

1. Xiaoxiao Li
2. Guofan Shao

Citations in the corpus

• UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis (2015, background): “The former assumes that individual pixels are independent and they are treated without considering any spatial association with neighboring pixels [16].”
• Object-based urban vegetation mapping with high-resolution aerial photography as a single data source (2013, methodology): “Recent research has reinforced the development and application of OBIA, which uses contextual spatial information such as texture and shape within a neighbourhood in the classification of high-resolution imagery (Cleve et al. 2008; Nichol and Wong 2008; PeñaBarragán et al. 2011).”