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16 Automatic classification of Very High Resolution images: state of the art Recently, in the machine learning community [105], the main difference is between methods based on: - Learning from experimental data (examples, samples, measurements, records, patterns, or observations) by neural networks (NNs) and support vector machines (SVMs); - Embedding existing structured human knowledge (experience, expertise, heuristics) into workable mathematics by fuzzy logic models (FLMs). This introduction is focused on the first category, considering the unsupervised approach to the problem. In this context, Transductive SVMs (TSVMs) [106], [107] and semisupervised SVMs (called also S 3 VMs) [108] proved particularly effective in several applications and they have been adapted in Remote Sensing. In [109], TSVMs have been applied to Landsat images classification, in [110] S 3 VMs have been focused to classify Hyperion data set and in [111] SVMs detected man-made objects from SPOT5 images. About NNs, their advantages will be discussed in section 1.4.1. Another methodology is represented by the decision tree: a Decision Tree Classifier (DTC) is a hierarchical classifier that recursively partitions the classification problem in smaller sub-problems on the basis of given statistical or empirical criteria. At each node of the tree a simple decision rule with predefined threshold values is used. In [112] this method has been used to classify VHR images. Appreciable results in automatic classification seem to derive also from the application of Self Organizing Map (SOM) developed by Kohonen [113], [114] during his long studies dedicated to this topic. Even if these particular unsupervised neural networks have been used in image analysis in the second part of the 80‘s,
Chapter 1 17 they begin to be used in remote sensing in [115] on Landsat images clusterization. Successively, these NN‘s helped for SPOT image characterization [116]. Another use of SOM can be found in Laaksonen [117] where a hierarchical SOM has been utilized in Content-Based-Image Retrieval (CBIR). Due to the tree structure, the number of map units increase when moving downwards the SOM levels of the TS (Tree Structured) SOM. This concept has been extended in [118] for building detection with spectral and textural parameters in a CBIR; which means that the methodology does not provide a direct delineation of objects of interest but could point out locations in the image where there may be potentially interesting structures. So, nowadays, there are no developed methods to classify automatically VHR image with SOM. 1.3.1 SOM A SOM can automatically form one- or multi-dimensional maps of intrinsic features of the input data. These data are presented in mixed random order to the network which is capable of learning complicated hierarchical relations within the considered high-dimensional spaces. In remote sensing, SOMs are used to identify measurements relationships which they can re-organize in several output cluster/classes. 1.3.1.1 Net structure A neural network consists in a series of inputs and in a n-dimensional grid of neurons. Each input is connected to all neurons of the grid: the resulting matrix of weights is used to propagate the inputs of the network to the neuron on the map.
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90 Pre-processing
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92 5.1 Vegetation Spectral, textura
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94 5.2 Water Spectral, textural and
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96 5.3 Bare Soil Spectral, textural
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98 5.4 Buildings Spectral, textural
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102 Spectral, textural and shape ch
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142 6.4.4 Bari (I) Pixel based clas
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150 Application of automatic classi
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168 Conclusions Conclusions In this
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170 Conclusions In fact, the traini
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172 Conclusions (i.e. topological r
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174 Bibliography [10] P. M. Teillet
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176 Bibliography [102] Geoeye, Ikon
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178 Bibliography [122] P. Dreyer,
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180 Bibliography [141] P. Gong, D.
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182 Bibliography [160] B. Guindon,
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184 Bibliography [307] F. Melgadi a
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186 Bibliography [407] Z. Qu, B. Ki
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188 Bibliography [424] C. Elachi,
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190 Bibliography [709] A. R. Gilles
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192 Bibliography
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194 List of acronyms and abbreviati
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196 CV, conferences and publication
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