0000000000124002
AUTHOR
L. Alonso-chorda
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 met…
<title>Methodology for quantitative analysis of scaling effects in multiresolution datasets acquired with airborne sensors flying at different altitude levels</title>
Scaling issues are always playing a critical role in most studies based on remote sensing data. The process of getting quantitative scaling information from raw multi-resolution images is not trivial, and many aspects must be taken very carefully into consideration. To get a better picture about the role of spatial resolution, we conducted a series of flights in summer 1997, in several test sites over Spain and Portugal. In order to minimize the time of acquisition (to get minimal changes in atmospheric status and solar illumination) we used three flight altitude levels, that produced images with 1.25 m, 3 m and 12 m resolutions. The main steps in our methodology are: a) Geometrical registr…