A survey of image classification methods and techniques for improving classification performance

By Dengsheng Lu, Qihao Weng. 2007

3,061 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/5f07f1301d6515464b35c0625ef3e4f05b240377

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Citations in the corpus

Range-wide analysis of wildlife habitat: Implications for conservation (2010, methodology): “However, this procedure is common in many pixel-based imagery classification methods (Lu and Weng, 2007).”
Deep learning for image classification on very small datasets using transfer learning (2019, methodology): “Effective use of multiple features of data and the selection of a suitable classification method [12] are significant for image classification.”
UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis (2015, background): “The definition of land cover classes strictly depends on the aim of the project, the study area and the resolution of the input data [30].”
Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “…for speci ﬁ c methods, such as Mountrakis, Im, and Ogole (2011) review of support vector machines (SVMs), and Belgiu and Dr ă gu ţ (2016) article about Random Forests (RFs), as well as articles reviewing the process of classi ﬁ cation and its complexities, such as that of Lu and Weng (2007).”
Sentinel-2 Data for Land Cover/Use Mapping: A Review (2020, background): “The major reason is that many classification algorithms have been developed based on the supervised classification approach, while the unsupervised classification employs the Iterative Self-Organizing Data Analysis Technique (ISODATA) and k-means clustering as the major classification algorithms [78”
Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues (2009, methodology): “0099-1112/09/7507–0819/$3.00/0 © 2009 American Society for Photogrammetry and Remote Sensing”
Multi-source remote sensing data fusion: status and trends (2010, neutral): “The integration of data from different application fields has to concern the differences in the object models and semantics of the objects themselves (Fonseca et al. 2002, Lu and Weng 2007).”
Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal (2010, background): “In various empirical studies, different classification techniques have been discussed and they can be categorized as being either supervised or un-supervised; parametric or non-parametric; hard or soft classifiers [10].”
Visual Classification by ℓ1-Hypergraph Modeling (2015, background): “That is, a hyperedge is generated by connecting a set of samples sharing a same feature attribute or in a neighborhood.”