A survey of image classification methods and techniques

By S. Nath, Jajnyaseni Kar, G. Mishra, Sayan Chakraborty, Nilanjan Dey. International Conference on Control, Instrumentation, Communication and Computational Technologies, 2014

516 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/3793583d345fb7348033d8250c96c7743f4445ef

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In Atmospheric Science

In Computer Vision and Pattern Recognition

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Citations in the corpus

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, background): “Furthermore, ANN recognition of pattern challenge occurs in rule and template matching kinds of paradigm [95-97].”
Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data (2019, unknown): “In order to apply such machine learning algorithms, appropriate architectural design or pre-processing steps must be predefined (Lu and Weng, 2007).”
Deep learning classifiers for hyperspectral imaging: A review (2019, unknown): “For instance, Lu and Weng (2007) offered an interesting and complete taxonomy of thirteen categories, considering six different criteria, while Chutia et al. (2016) presented a simpler taxonomy of six different groups depending on the classification procedure.”
Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification (2017, unknown): “Lu and Weng [13] provide a comprehensive survey on classiﬁcation methods.”
Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers (2014, methodology): “This is a common problem when classifying heterogeneous landscapes using high spatial resolution based on per-pixel classification techniques (Lu and Weng 2007; Duro, Franklin, and Dubé 2012).”
A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring (2016, background): “In general, fusion refers to a formal concept for combining data from different sources [49,50], with the aim of generating information of “greater quality” than the individual input datasets.”
Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information (2014, methodology): “Overall accuracy and Cohen's kappa statistic (Cohen, 1960) are commonly used to evaluate classifier performance (Lu and Weng, 2007).”
Medical hyperspectral imaging: a review (2014, background): “Parametric classifiers generally assume normal distribution for the data, which is often violated in practice.(136) Nonparametric methods, such as support vector machines (SVMs) and artificial neural networks (ANN) are widely used in medical hyperspectral image processing.”
A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research (2016, unknown): “For example, Lu and Weng (2007) and Weng (2012) discussed details of major image classi-ﬁ cation approaches and their main steps, classi ﬁ cation accuracy improvement techniques and issues affecting classi ﬁ cation performance.”
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery (2014, background): “…to map land cover from satellite imagery, and new algorithm proposers have reported improvements in accuracies of their mapping experiments (see [1] for a substantial review on classification algorithms), it is difficult to find a systematic comparison on the performance of newly proposed…”