Mid-Infrared Galaxy Morphology Along the Hubble Sequence

The mid-infrared emission from 18 nearby galaxies imaged with the IRAC instrument on Spitzer Space Telescope samples the spatial distributions of the reddening-free stellar photospheric emission and the warm dust in the ISM. These two components provide a new framework for galaxy morphological classification, in which the presence of spiral arms and their emission strength relative to the starlight can be measured directly and with high contrast. Four mid-infrared classification methods are explored, three of which are based on quantitative global parameters (colors, bulge-to-disk ratio) similar to those used in the past for optical studies; in this limited sample, all correlate well with traditional B-band classification. We suggest reasons why infrared classification may be superior to optical classification.Comment: ApJS (in press), Spitzer Space Telescope Special Issue; 13 pages, LaTeX (or Latex, etc); Figure 1ab is large, color plate; full-resolution plates in .pdf format available at http://cfa-www.harvard.edu/irac/publications

Remote sensing/vegetation classification

The CALVEG classification system for identification of vegetation is described. This hierarchical system responds to classification requirements and to interpretation of vegetation at various description levels, from site description to broad identification levels. The system's major strength is its flexibility in application of remote sensing technology to assess, describe and communicate data relative to vegetative resources on a state-wide basis. It is concluded that multilevel remote sensing is a cost effective tool for assessment of the natural resource base. The CLAVEG system is found to be an economically efficient tool for both existing and potential vegetation

Colour appearance descriptors for image browsing and retrieval

In this paper, we focus on the development of whole-scene colour appearance descriptors for classification to be used in browsing applications. The descriptors can classify a whole-scene image into various categories of semantically-based colour appearance. Colour appearance is an important feature and has been extensively used in image-analysis, retrieval and classification. By using pre-existing global CIELAB colour histograms, firstly, we try to develop metrics for wholescene colour appearance: “colour strength”, “high/low lightness” and “multicoloured”. Secondly we propose methods using these metrics either alone or combined to classify whole-scene images into five categories of appearance: strong, pastel, dark, pale and multicoloured. Experiments show positive results and that the global colour histogram is actually useful and can be used for whole-scene colour appearance classification. We have also conducted a small-scale human evaluation test on whole-scene colour appearance. The results show, with suitable threshold settings, the proposed methods can describe the whole-scene colour appearance of images close to human classification. The descriptors were tested on thousands of images from various scenes: paintings, natural scenes, objects, photographs and documents. The colour appearance classifications are being integrated into an image browsing system which allows them to also be used to refine browsing