Detection of leaf structures in close-range hyperspectral images using morphological fusion

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods

Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods

Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201

Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs

Quasar Candidates in the Hubble Deep Field

We focus on the search for unresolved faint quasars and AGN in the crude combine images using a multicolor imaging analysis that has proven very successful in recent years. Quasar selection was carried out both in multicolor space and in "profile space," defined as the multi-parameter space formed by the radial profiles of the objects in the different images. By combining the dither frames available for each filter, we were able to obtain well-sampled radial profiles of the objects and measure their deviation from that of a stellar source. We also generated synthetic quasar spectra in the range 1.0 < z < 5.5 and computed expected quasar colors. We determined that the data are 90% complete for point sources at 26.2, 28.0, 27.8, 26.8 in the F300W, F450W, F606W and F814W filters, respectively. We find 41 compact objects in the HDF: 8 pointlike objects with colors consistent with quasars or stars, 18 stars, and 15 slightly resolved objects, 12 of which have colors consistent with quasars or stars. We estimate the upper limit of unresolved and slightly resolved quasars/AGNs with V < 27.0 and z < 3.5 to be 20 objects (16,200 per deg^2). We find good agreement among authors on the number of stars and the lack of quasar candidates with z > 3.5. We find more quasar candidates than previous work because of our more extensive modeling and use of all of the available color information. (abridged)Comment: We have clarified our discussion and conclusions, added some references and removed the appendix, which is now available from the first author. 37 pages including 10 embedded postscript figures and 6 tables. To appear in the Feb. 99 issue of A

A semantic-based platform for the digital analysis of architectural heritage

This essay focuses on the fields of architectural documentation and digital representation. We present a research paper concerning the development of an information system at the scale of architecture, taking into account the relationships that can be established between the representation of buildings (shape, dimension, state of conservation, hypothetical restitution) and heterogeneous information about various fields (such as the technical, the documentary or still the historical one). The proposed approach aims to organize multiple representations (and associated information) around a semantic description model with the goal of defining a system for the multi-field analysis of buildings