Abstract: With the development of HyperSpectral Imagery
(HSI) technology, the spectral resolution of HSI became denser,
which resulted in large number of spectral bands, high correlation
between neighboring, and high data redundancy. However, the
semantic interpretation is a challenging task for HSI analysis
due to the high dimensionality and the high correlation of the
different spectral bands. In fact, this work presents a dimensionality
reduction approach that allows to overcome the different issues
improving the semantic interpretation of HSI. Therefore, in order
to preserve the spatial information, the Tensor Locality Preserving
Projection (TLPP) has been applied to transform the original HSI.
In the second step, knowledge has been extracted based on the
adjacency graph to describe the different pixels. Based on the
transformation matrix using TLPP, a weighted matrix has been
constructed to rank the different spectral bands based on their
contribution score. Thus, the relevant bands have been adaptively
selected based on the weighted matrix. The performance of the
presented approach has been validated by implementing several
experiments, and the obtained results demonstrate the efficiency
of this approach compared to various existing dimensionality
reduction techniques. Also, according to the experimental results,
we can conclude that this approach can adaptively select the
relevant spectral improving the semantic interpretation of HSI.
Abstract: This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.