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| ===== Multi-Temporal Analysis ===== | ===== Multi-Temporal Analysis ===== | ||
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| + | ==== Automatic Change Detection in Urban Areas Under a Scale-Space, Object-Oriented Classification Framework ==== | ||
| + | //GEOBIA, 2010.// | ||
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| + | //Doxani, G., Karantzalos, K., Tsakiri-Strati, M.// | ||
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| + | The work presents and object-based classification framework to detect building constructions using two different times of remote sensing imagery. The method applies the Multivariate Alteration Detection (MAD), which is a correlation analysis between two groups of variables (images). The obtained MAD components are the difference of the corresponding input images, which depict the same area and were acquired at different dates. | ||
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| + | MAD components with values higher than a certain threshold |two times the standard deviation| correspond to changing pixels. Such pixels are then analysed further to infer changes regarding building constructions. They tested using single pixels, and also the spectral information obtained by segmentation in different scales. | ||
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| + | ==== Trajectory of Dynamic Clusters in Image Time-Series ==== | ||
| + | //MultiTemp, 2003.// | ||
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| + | //Heas, P., Datcu, M., Giros, A.// | ||
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| + | This work performs two types of classification using image time-series. | ||
| + | The first one is called time-localized clustering, where each time-window in the series possesses a corresponding classification. Such classification neglects the information about the causalities between different times. | ||
| + | The second is called multi-temporal classification, where information from all images are used to perform the classification. | ||
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| + | The authors argue that it is possible to create a model which is capable of measuring the distance between the multi-temporal clusters and the time-localized clusters. With this model it is possible to trace the cluster evolutions. They calculate the cross entropy between each multi-temporal cluster and all the time-localized ones. The maximum entropy is used to match both types of clusters. | ||
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| ==== Modeling Cyclic Change ==== | ==== Modeling Cyclic Change ==== | ||
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| ===== Data Mining ===== | ===== Data Mining ===== | ||
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| + | ==== Decision tree regression for soft classification of remote sensing data ==== | ||
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| + | //Remote Sensing of Environment, 2005.// | ||
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| + | //Xu, M., Watanachaturaporn, P., Varshney, P. Arora, M.// | ||
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| + | This work proposes a decision tree with soft thresholds, which decompose the pixel into its class constituents in the form of class proportions. The outputs from soft classification provide a set of fraction images that represent proportion of classes for each pixel. The use of decision trees is justified because remote sensing data often not follow Gaussian distribution, an assumption made by several classifiers. | ||
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| + | To build the class proportions, the authors suggest to create several decision trees, called forest. Every class proportion will derive one soft decision tree in the forest. Concluding, the authors argue that the tree construction is very fast and is, therefore, suitable for classifying large remote sensing images. | ||
| ==== Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales ==== | ==== Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales ==== | ||
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| Another suggestion is to use "feature extraction", which means creating new features by applying combinations on already existent features. However, "although extracted features have higher discrimination power, their physical meanings were hard to explain, which lowered the interpretability of classification trees". | Another suggestion is to use "feature extraction", which means creating new features by applying combinations on already existent features. However, "although extracted features have higher discrimination power, their physical meanings were hard to explain, which lowered the interpretability of classification trees". | ||
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| + | ===== Image Processing ===== | ||
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| + | ==== High spatial resolution spectral mixture analysis of urban reflectance ==== | ||
| + | //Remote Sensing of Environment, 2003.// | ||
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| + | //Small, C.// | ||
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| + | The author explores the use of IKONOS imagery for intraurban classification. Medium spatial resolution sensors, such as TM and SPOT present abundance of mixed pixels. However, mixed pixels are problematic for statistical classification methods because most algorithms are based on the assumption of spectral homogeneity at pixel scale within a particular class of land cover, but urban areas provide examples of spectrally diverse, scale-dependent thematic classes containing large numbers of pixels that are spectrally indistinguishable from other land cover classes. The work proposes the use of spatial autocorrelation to quantify the characteristic scale lengths of urban reflectance within and among different cities. The question of the paper is if the consistency of the spectral mixing space for a urban areas can be provided by a simple three-component linear mixture model, to characterize the urban reflectance. Concluding, it is argued that spectral mixture analysis is preferable to "hard classification" for many applications because it accommodates the preponderance of mixed pixels observed in almost all multispectral imagery. | ||
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