Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery

By Soe Win Myint, P. Gober, A. Brazel, S. Grossman‐Clarke, Qihao Weng. Remote Sensing of Environment, 2011

1,282 citations recorded by the source corpus.

In using traditional digital classification algorithms, a researcher typically encounters serious issues in identifying urban land cover classes employing high resolution data. A normal approach is to use spectral information alone and ignore spatial information and a group of pixels that need to be considered together as an object. We used QuickBird image data over a central region in the city of Phoenix, Arizona to examine if an object-based classifier can accurately identify urban classes. To demonstrate if spectral information alone is practical in urban classification, we used spectra of the selected classes from randomly selected points to examine if they can be effectively discriminated. The overall accuracy based on spectral information alone reached only about 63.33%. We employed five different classification procedures with the object-based paradigm that separates spatially and spectrally similar pixels at different scales. The classifiers to assign land covers to segmented objects used in the study include membership functions and the nearest neighbor classifier. The object-based classifier achieved a high overall accuracy (90.40%), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (67.60%). This study demonstrates that the object-based classifier is a significantly better approach than the classical per-pixel classifiers. Further, this study reviews application of different parameters for segmentation and classification, combined use of composite and original bands, selection of different scale levels, and choice of classifiers. Strengths and weaknesses of the object-based prototype are presented and we provide suggestions to avoid or minimize uncertainties and limitations associated with the approach.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/0bed9089e9bd9f577ced241e82ff4e0eac550995

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Citations in the corpus

Deep learning in environmental remote sensing: Achievements and challenges (2020, background): “Nagamani et al. (2007) found that the ANN model is effective in modeling the non-linear relationship between nLw and chlorophyll-a concentration.”
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery (2014, background): “A considerable amount of research has shown that object-based approaches are superior to traditional pixel-based methods in the classification of high spatial resolution data [1–4].”
A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, methodology): “Compared to traditional per-pixel and sub-pixel classification methods, object-based models provide a new paradigm to classify remote sensing imagery [Blaschke, 2010; Myint et al., 2011] (See Tab.”
Identifying Santa Barbara's urban tree species from AVIRIS imagery using canonical discriminant analysis (2013, background): “Crown-level, as opposed to pixel-level, classification may further enhance our ability to make useful maps, with less ‘salt and pepper’ noise (Benz et al. 2004, Myint et al. 2011).”
A Survey on Object Detection in Optical Remote Sensing Images (2016, neutral): “Table 2 A brief summary of knowledge-based object detection methods”
Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification (2013, background): “Because spectral information of pixels is often insufficient to identify detailed classes in these images (Wyint et al. 2011), meaningful image objects and their relationships need to be understood and applied to image analysis.”
Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine (2017, background): “Earlier, several authors [49,50] have explored this combination of methods for land use/land cover classification over large areas, but not for cropland classification over large areas yet.”