A Comprehensive Review on Pixel Oriented and Object Oriented Methods for Information Extraction from Remotely Sensed Satellite Images with a Special Emphasis on Cryospheric Applications

Abstract

Image classification is one of the most basic operations of digital image processing. The present review focuses on the strengths and weaknesses of traditional pixel-based classification (PBC) and the advances of object-oriented classification (OOC) algorithms employed for the extraction of information from remotely sensed satellite imageries. The state-of-the-art classifiers are reviewed for their potential usage in urban remote sensing (RS), with a special focus on cryospheric applications. Generally, classifiers for information extraction can be divided into three catalogues: 1) based on the type of learning (supervised and unsupervised), 2) based on assumptions on data distribution (parametric and non-parametric) and, 3) based on the number of outputs for each spatial unit (hard and soft). The classification methods are broadly based on the PBC or the OOC approaches. Both methods have their own advantages and disadvantages depending upon their area of application and most importantly the RS datasets that are used for information extraction. Classification algorithms are variedly explored in the cryosphere for extracting geospatial information for various logistic and scientific applications, such as to understand temporal changes in geographical phenomena. Information extraction in cryospheric regions is challenging, accounting to the very similar and conflicting spectral responses of the features present in the region. The spectral responses of snow and ice, water, and blue ice, rock and shadow are a big challenge for the pixel-based classifiers. Thus, in such cases, OOC approach is superior for extracting information from the cryospheric regions. Also, ensemble classifiers and customized spectral index ratios S. D. Jawak et al. 178 (CSIR) proved extremely good approaches for information extraction from cryospheric regions. The present review would be beneficial for developing new classifiers in the cryospheric environment for better understanding of spatial-temporal changes over long time scales.

Object-oriented feature extraction approach for mapping supraglacial debris in Schirmacher Oasis using very high-resolution satellite data

“…Out of those eight bands Red, Green, blue, NIR1 are the already existing bands and the new ones are RedEdge, Yellow, Coastal and NIR2. These new spectral bands facilitate high precision in spectral analysis for land use/ land cover mapping, cryospheric information extraction, visualization and simulation environments, bathymetry derivations [22][23][24][25][26][27] . The spectral and spatial range of various bands of WV-2 are depicted on Table 1.…”

Geospatial mapping of vegetation in the Antarctic environment using very high-resolution WorldView-2 imagery

“…RedEdge (705-745 nm), Yellow (585-625 nm), Coastal (400-450 nm) and NIR2 (860-1040 nm) are the new bands. These new spectral bands facilitate high precision in spectral analysis for land use/ land cover mapping, cryospheric information extraction, visualization and simulation environments, bathymetry derivations [28][29][30][31][32][33][34] . The spectral and spatial ranges of various bands of WorldView-2 are depicted on Table 1.…”

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