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אסיף מאגר המחקר החקלאי
פותח על ידי קלירמאש פתרונות בע"מ -
Spectral and Spatial Methods for Hyperspectral and Thermal Image-Analysis to Estimate Biophysical and Biochemical Properties of Agricultural Crops
Year:
2018
Authors :
אלחנתי, ויקטור
;
.
כהן, יפית
;
.
Volume :
3
Co-Authors:
Facilitators :
From page:
73
To page:
101
(
Total pages:
29
)
Abstract:

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.

Note:

Chapter 3

Related Files :
Agricultural crops
Biophysical properties
spatial analysis
spectral analysis
Thermal imagery
עוד תגיות
תוכן קשור
More details
DOI :
Article number:
0
Affiliations:
Database:
Publication Type:
פרק מתוך ספר
;
.
Language:
אנגלית
Editors' remarks:
ID:
49343
Last updated date:
02/03/2022 17:27
Creation date:
31/08/2020 10:26
Scientific Publication
Spectral and Spatial Methods for Hyperspectral and Thermal Image-Analysis to Estimate Biophysical and Biochemical Properties of Agricultural Crops
3
Spectral and Spatial Methods for Hyperspectral and Thermal Image-Analysis to Estimate Biophysical and Biochemical Properties of Agricultural Crops

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

Scientific Publication
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