An improved hybrid clustering algorithm for natural scenes

Simpson, Joseph; McIntire, T.J.; Sienko, M.

doi:10.1109/36.841982

Cited by 25 publications

(32 citation statements)

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“…Early hybrid methods were developed using Landsat images and became common in the 1980s just after the development of supervised and unsupervised classification. However, the development of more advanced classifiers in the last decade has made the hybrid approach more diverse and powerful [60,61]. In most cases, the results from hybrid approaches depend on a number of factors such as quality of pre-processing, experience of an analyst and performance of the classifiers.…”

Section: Multiple (Hybrid) Classifier Approachesmentioning

confidence: 99%

Developments in Landsat Land Cover Classification Methods: A Review

2017

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Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

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Section: Multiple (Hybrid) Classifier Approachesmentioning

confidence: 99%

Developments in Landsat Land Cover Classification Methods: A Review

2017

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“…Another powerful approach to cloud screening has been described (Simpson et al 2000) based on identifying the natural clusters of pixel properties in scenes, and then adaptively labelling these as clear or cloudy. This is interesting in that it seems to offer a 'prior-free' alternative; the classification emerges from the properties of the imagery.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic physically based cloud screening of satellite infrared imagery for operational sea surface temperature retrieval

Merchant¹,

Harris²,

Maturi³

et al. 2005

Quart J Royal Meteoro Soc

141

153

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SUMMARYWe propose and demonstrate a fully probabilistic (Bayesian) approach to the detection of cloudy pixels in thermal infrared (TIR) imagery observed from satellite over oceans. Using this approach, we show how to exploit the prior information and the fast forward modelling capability that are typically available in the operational context to obtain improved cloud detection. The probability of clear sky for each pixel is estimated by applying Bayes' theorem, and we describe how to apply Bayes' theorem to this problem in general terms. Joint probability density functions (PDFs) of the observations in the TIR channels are needed; the PDFs for clear conditions are calculable from forward modelling and those for cloudy conditions have been obtained empirically. Using analysis fields from numerical weather prediction as prior information, we apply the approach to imagery representative of imagers on polar-orbiting platforms. In comparison with the established cloud-screening scheme, the new technique decreases both the rate of failure to detect cloud contamination and the false-alarm rate by one quarter. The rate of occurrence of cloud-screening-related errors of >1 K in area-averaged SSTs is reduced by 83%.

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“…Cloud, glint and clear ocean classes were identified in random ATSR-2 scenes using the fully automated, multispectral, hybrid clustering (MSHC) algorithm developed by Simpson et al (2000). This algorithm uses an improved hybrid clustering scheme (unsupervised analysis) to segment the scene into a set of statistically homogeneous (i.e., within cluster variance is small) but distinct (i.e., between cluster variance is large) clusters.…”

Section: Design Of Input Information Vectorsmentioning

confidence: 99%