Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis

P. S. Chavez, A. Y. Kwarteng

نتاج البحث: المساهمة في مجلةمراجعة النظراء

593 اقتباسات (Scopus)

ملخص

A challenge encountered with Landsat Thematic Mapper (TM) data, which includes data from size reflective spectral bands, is displaying as much information as possible in a three-image set for color compositing or digital analysis. Principal component analysis (PCA) applied to the six TM bands simultaneously is often used to address this problem. However, two problems that can be encountered using the PCA method are that information of interest might be mathematically mapped to one of the unused components and that a color composite can be difficult to interpret. "Selective' PCA can be used to minimize both of these problems. The spectral contrast among several spectral regions was mapped for a northern Arizona site using Landsat TM data. Field investigations determined that most of the spectral contrast seen in this area was due to one of the following: the amount of iron and hematite in the soils and rocks, vegetation differences, standing and running water, or the presence of gypsum, which has a higher moisture retention capability than do the surrounding soils and rocks. -from Authors

اللغة الأصليةEnglish
الصفحات (من إلى)339-348
عدد الصفحات10
دوريةPhotogrammetric Engineering & Remote Sensing
مستوى الصوت55
رقم الإصدار3
حالة النشرPublished - 1989

ASJC Scopus subject areas

  • ???subjectarea.asjc.1900.1903???

بصمة

أدرس بدقة موضوعات البحث “Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis'. فهما يشكلان معًا بصمة فريدة.

قم بذكر هذا