Classification of hyperspectral imagery with neural networks: comparison to conventional tools

Efficient exploitation of hyperspectral imagery is of great importance in remote sensing. Artificial intelligence approaches have been receiving favorable reviews for classification of hyperspectral data because the complexity of such data challenges the limitations of many conventional methods. Artificial neural networks (ANNs) were shown to outperform traditional classifiers in many situations. However, studies that use the full spectral dimensionality of hyperspectral images to classify a large number of surface covers are scarce if non-existent. We advocate the need for methods that can handle the full dimensionality and a large number of classes to retain the discovery potential and the ability to discriminate classes with subtle spectral differences. We demonstrate that such a method exists in the family of ANNs. We compare the maximum likelihood, Mahalonobis distance, minimum distance, spectral angle mapper, and a hybrid ANN classifier for real hyperspectral AVIRIS data, using the full spectral resolution to map 23 cover types and using a small training set. Rigorous evaluation of the classification accuracies shows that the ANN outperforms the other methods and achieves ?90% accuracy on test data

Data topology visualization for the self-organizing map

Abstract. The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, is very useful for processing data of high dimensionality and complexity. Visualization methods present different aspects of the information learned by the SOM to gain insight and guide segmentation of the data. In this work, we propose a new visualization scheme that represents data topology superimposed on the SOM grid, and we show how it helps in the discovery of data structure. 1 Visualization of SOM knowledge The Self-Organizing Map (SOM) [1] is a widely and successfully used neural paradigm for clustering and data mining. Informative representation of the learned SOM’s knowledge greatly aids precise capture of the cluster boundaries. This is especially important for high-dimensional and large data sets with many meaningful clusters such as in remote sensing or medical imagery, which often also have interesting rare clusters to be discovered. An impressive suite of previous works include the U-matrix [2] and its variants

knowledge through precision manifold learning, and application to onboard

decision suppor

Forbidden magnification? I

Abstract. This paper presents some interesting results obtained by the algorithm by Bauer, Der and Hermann (BDH) [1] for magnification control in Self-Organizing Maps. Magnification control in SOMs refers to the modification of the relationship between the probability density functions of the input samples and their prototypes (SOM weights). The above mentioned algorithm enables explicit control of the magnification properties of a SOM, however, the available theory restricts its validity to 1-D data or 2-D data when the stimulus density separates. This discourages the use of the BDH algorithm for practical applications. In this paper we present results of careful simulations that show the scope of this algorithm when applied to more general, ”forbidden ” data. We also demonstrate the application of negative magnification to magnify rare classes in the data to enhance their detectability.

Forbidden magnification? ii

Abstract. The twin of this paper, “Forbidden Magnification? I.” [1], presents systematic SOM simulations with the explicit magnification control scheme of Bauer, Der, and Herrmann [2] on data for which the theory does not guarantee success, namely data that are n-D, n> 2 and/or data whose components in the different dimensions are not statistically independent. For the unsupported n = 2 cases that we investigated the simulations show that even though the magnification exponent αachieved achieved by magnification control is not the same as the intended αintended, the direction and sign of αachieved systematically follows αintended with a more or less constant offset. We experimentally showed that for simple synthetic higher dimensional data negative magnification has the desired effect of improving the detectability of rare classes. In this paper we study further theoretically unsupported cases, including experiments with real data. 1 Known limits of SOM magnification contro