literature survey on image classification

Uncertainty may be modelled or quantified in different ways such as fuzzy and probabilistic classification techniques, or via visualization (van der Wel et al. Texture, shape, and context information are currently most frequently used. Reusing back‐propagating artificial neural network for land cover classification in tropical savannahs. [5], the paper studies the development of Deep CNN (Convolutional Neural Network) and to match its image classification performance with the performance of the dermatologists. Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. mean vector and covariance matrix) generated from the training samples are representative. Supervised iterative classification (multistage classification), MFM‐5‐Scale (Multiple‐Forward‐Mode approach to running the 5‐Scale geometric‐optical reflectance model), Iterative partially supervised classification based on a combined use of a Radial Basis Function network and a Markov Random Field approach, Classification by progressive generalization, Unsupervised classification based on independent component analysis mixture model, Optimal iterative unsupervised classification, Multispectral classification based on probability density functions, Fuzzy‐based multisensor data fusion classifier, Linear regression or linear least squares inversion, Per‐field classification based on per‐pixel or subpixel classified image, Parcel‐based approach with two stages: per‐parcel classification using conventional statistical classifier and then knowledge‐based correction using contextual information, Graph‐based, structural pattern recognition system, ECHO (Extraction and Classification of Homogeneous Objects), Contextual classification approaches for high and low resolution data, respectively and a combination of both approaches, Contextual classifier based on region‐growth algorithm, Hybrid approach incorporating contextual information with per‐pixel classification, Visual fuzzy classification based on use of exploratory and interactive visualization techniques, Multitemporal classification based on decision fusion, Supervised classification with ongoing learning capability based on nearest neighbour rule, Combinative approaches of multiple classifiers, Multiple classifier system (BAGFS: combines bootstrap aggregating with multiple feature subsets), A consensus builder to adjust classification output (MLC, expert system, and neural network), Integrated expert system and neural network classifier, Improved neuro‐fuzzy image classification system, Mixed contextual and per‐pixel classification, Combination of iterated contextual probability classifier and MLC, Combination of neural network and statistical consensus theoretic classifiers, Combination of MLC and neural network using Bayesian techniques, Combining multiple classifiers based on product rule, staked regression, Combined spectral classifiers and GIS rule‐based classification, Combination of MLC and decision tree classifier, Combination of non‐parametric classifiers (neural network, decision tree classifier, and evidential reasoning), Combined supervised and unsupervised classification, First‐, second‐, and third‐order statistics in the spatial domain; texture features from the texture spectrum and from grey level different vector, Co‐occurrence matrices, grey‐level difference, texture‐tone analysis, features derived from Fourier spectrum, and Gabor filters, GLCM, grey level difference histogram, sum and different histogram, Triangulated primitive neighbourhood method, Fusion of multisensor or multiresolution data, Using multitemporal optical and SAR images, Data obtained from FieldSpec Pro FR spectroradiometer, Based on illumination and ecological zone, Using zoning and housing density data to modify the initial classification result, Using filtering based on co‐occurrence matrix, Using polygon and rectangular mode filters, Using expert system to perform post‐classification sorting, Using knowledge‐based system to correct misclassification, Spectral, texture, and ancillary data (such as DEM, soil, existing GIS‐based maps). 1982, Civco 1989, Colby 1991, Meyer et al. Integrating topographic data with remote sensing for land‐cover classification. Vegetation indices, principal component analysis, tasselled cap, and minimum noise fraction, are among the most commonly used ones (Oetter et al. Multisource classification of complex rural areas by statistical and neural‐network approaches. The neighboring class labels of a given pixel in the non-contextual classification map are exploited to extract spatial information, while temporal information is deduced from the non-contextual maps produced by the remaining single-time images. Similarly, temperature, precipitation, and soil data are related to land‐cover distribution at a large scale. Ancillary data, such as topography, soil, road, and census data, may be combined with remotely sensed data to improve classification performance. AVIRIS and EO‐1 Hyperion images with 224 bands). Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications. Data fusion or integration of multisensor or multiresolution data takes advantage of the strengths of distinct image data for improvement of visual interpretation and quantitative analysis.

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