Juan Carlos De Martin

Feature selection of hyperspectral data through local correlation and SFFS for crop classification

In this paper, we propose a procedure to reduce dimensionality of hyperspectral data while preserving relevant information for posterior crop cover classification. One of the main problems with hyperspectral image processing is the huge amount of data involved. In addition, pattern recognition methods are sensitive to problems associated to high dimensionality feature spaces (referred to as Hughes phenomenon of curse of dimensionality). We propose a dimensionality reduction strategy that eliminates redundant information by means of local correlation criterion between contiguous spectral bands; and a subsequent selection of the most discriminative features based on a Sequential Float Feature Selection algorithm. This method is tested with a crop cover recognition application of six hyperspectral images from the same area acquired with the 128-bands HyMap spectrometer during the DAISEX99 campaign. In the experiments, we analyze the dependence on the dimension and employed metrics. The results obtained using the Gaussian Maximum Likelihood improve the classification accuracy and confirm the validity of the proposed approach. Finally, we analyze the selected bands of the input space on order to gain knowledge on the problem and to give a physical interpretation of the results.

CART-based feature selection of hyperspectral images for crop cover classification

In this paper, we propose a procedure to reduce data dimensionality while preserving relevant information for posterior crop cover classification. The huge amount of data involved in hyperspectral image processing is one of the main problems in order to apply pattern recognition techniques. We propose a dimensionality reduction strategy that eliminates redundant information and a subsequent selection of the most discriminative features based on classification and regression trees (CART). CART allow feature selection based on the classification success, it is a non-linear method and specially allows knowledge discovery. The main advantage of our proposal relies on model interpretability, since we can get qualitative information by analyzing the surrogate and main splits of the tree. This method is tested with a crop cover recognition application of six hyperspectral images from the same area acquired with the 128-bands HyMap spectrometer. Even though CART do not provide the best results in classification it is useful for a previous pre-processing step of feature selection. Finally, we analyze the selected bands of the input space in order to gain knowledge on the problem and to give a physical interpretation of results.

Semi-supervised classification method for hyperspectral remote sensing images

A new approach to the classification of hyperspectral images is proposed. The main problem with supervised methods is that the learning process heavily depends on the quality of the training data set. In remote sensing, the training set is useful only for simultaneous images or for images with the same classes taken under the same conditions; and, even worse, the training set is frequently not available. On the other hand, unsupervised methods are not sensitive to the number of labelled samples since they work on the whole image. Nevertheless, relationship between clusters and classes is not ensured. In this context, we propose a combined strategy of supervised and unsupervised learning methods that avoids these drawbacks and automates the classification process. The method is based on the general formulation of the expectation-maximization (EM) algorithm. This method is applied to crop cover recognition of six hyperspectral images from the same area acquired with the HyMap spectrometer during the DAISEX-99 campaign. For classification purposes, six different classes are considered. Classification accuracy results are compared to common methods: ISODATA, Learning Vector Quantization, Gaussian Maximum Likelihood, Expectation-Maximization, and Neural Networks. The good performance confirms the validity of the proposed approach in terms of accuracy and robustness.