תפריט נגישות
ניגודיות עדינהניגודיות גבוהה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם תפריט נגישותניגודיות עדינהניגודיות גבוההמונוכרוםהדגשת קישוריםחסימת אנימציהפונט קריאסגוראיפוס הגדרות נגישותלהורדת מודול נגישות חינםThe ultimate goals of hyperspectral and thermal sensing in precision agriculture are to estimate biophysical and biochemical properties of agricultural crops (BB-PACs: phonetically pronounced as bee-bee-pax) and to delineate and characterize homogeneous management zones for optimal agricultural management such fertilization, irrigation, or other agrotechnical operations. On the face of it, the use of hyperspectral and thermal remote sensing for precision agriculture seems to be similar to their use for natural vegetation. But for natural vegetation, remote sensing is widely used for classification of natural vegetation types, while in precision agriculture it is aimed at quantification of BB-PACs. The different goals are pursued by adapted analysis approaches. This chapter concentrates on three characteristics of hyperspectral (HS) images: First, their unique spectral properties, namely the narrow bandwidths and the plethora of the bands, as opposed to wider and limited number of bands in other broad-band spectral sensing systems such most multi-spectral satellite images; Second, the spatial attribute of hyperspectral images, as opposed to point spectral measurements of other spectral systems; and third, the state-of-the-art algorithms for hyperspectral image processing that show the added value of spatial information when combined with spectral information for mapping plant BB-PACs. In addition we present thermal panchromatic imaging as an image type complementary to the hyperspectral images.
Chapter 3
The ultimate goals of hyperspectral and thermal sensing in precision agriculture are to estimate biophysical and biochemical properties of agricultural crops (BB-PACs: phonetically pronounced as bee-bee-pax) and to delineate and characterize homogeneous management zones for optimal agricultural management such fertilization, irrigation, or other agrotechnical operations. On the face of it, the use of hyperspectral and thermal remote sensing for precision agriculture seems to be similar to their use for natural vegetation. But for natural vegetation, remote sensing is widely used for classification of natural vegetation types, while in precision agriculture it is aimed at quantification of BB-PACs. The different goals are pursued by adapted analysis approaches. This chapter concentrates on three characteristics of hyperspectral (HS) images: First, their unique spectral properties, namely the narrow bandwidths and the plethora of the bands, as opposed to wider and limited number of bands in other broad-band spectral sensing systems such most multi-spectral satellite images; Second, the spatial attribute of hyperspectral images, as opposed to point spectral measurements of other spectral systems; and third, the state-of-the-art algorithms for hyperspectral image processing that show the added value of spatial information when combined with spectral information for mapping plant BB-PACs. In addition we present thermal panchromatic imaging as an image type complementary to the hyperspectral images.
Chapter 3
