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Comparing Classification Results of Multi-Seasonal TM against AVIRIS Imagery – Seasonality more Important than Number of Bands

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Abstract:

We classified forest cover and tree den sity in the Black Hills, SD, in twenty spatially contiguous AVIRIS scenes. Results were compared to those derived from two-season Landsat TM imagery. A decision tree classifier was used to analyze the TM data as well as the over two hundred bands of the twenty AVIRIS scenes. The classification based on summer AVIRIS data was more accurate than the classification based on the comparable early fall TM data. However, classification of spring and especially, two-season TM data resulted in higher accuracies than the classification based on summer hyperspectral data. These results indicate that seasonality is more important than the number of spectral bands.

German
Die Waldfläche und Baumdichte in den Black Hills, South Dakota wurde in zwanzig räumlich zusammenhängenden AVIRIS Szenen klassifiziert. Diese Resultate wurden mit jahreszeitlich verschiedenen Landsat TM Bildern verglichen. Die TM Daten und die über zweihundert Bänder der zwanzig AVIRIS Szenen wurden anhand einer Entscheidungsbaum-Klassifizierung (decision-tree) analysiert. Aus den Ergebnissen lässt sich zeigen, dass die im Sommer aufgenom menen AVIRIS Klassen eine höhere Genauigkeit also die Frühherbst TM Daten aufweisen. Allerdings sind die Ergebnisse für TM besser, wenn Frühlingsdaten herangezogen werden. Die TM Kombination von Frühling und Herbst hat insgesamt die höchste Genauigkeit. Daraus lässt sich ableiten, dass Jahreszeit wichtiger als die Anzahl der Spektralbänder ist.

Keywords: AVIRIS; CLASSIFICATION; FOREST; LANDSAT TM; MULTITEMPORAL

Document Type: Research Article

DOI: http://dx.doi.org/10.1127/1432-8364/2012/0142

Publication date: October 1, 2012

More about this publication?
  • Photogrammetrie - Fernerkundung - Geoinformation (PFG) is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the intricately connected field of geoinformation processing.

    Papers published in PFG highlight new developments and applications of these technologies in practice. The journal hence addresses both researchers and student of these disciplines at academic institutions and universities and the downstream users in both the private sector and public administration.

    PFG places special editorial emphasis on the communication of new methodologies in data acquisition, new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general.

    PFG is the official journal of the German Society of Photogrammetry and Remote Sensing.
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