Change monitoring of agricultural lands using temporal hyperspectral datasets via feature extraction and classification techniques (Record no. 6164)

MARC details
000 -LEADER
fixed length control field 01959nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122843.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180719b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Anju Francis (92316001)
9 (RLIN) 14541
245 ## - TITLE STATEMENT
Title Change monitoring of agricultural lands using temporal hyperspectral datasets via feature extraction and classification techniques
300 ## - PHYSICAL DESCRIPTION
Extent MSC GIS 2016-2018
500 ## - GENERAL NOTE
General note <br/>Hyperspectral remote sensing, also known as imaging spectroscopy is one of the most significant <br/>breakthrough in remote sensing which Began in 1980’s. It is emerged as a technology for studying<br/>earth surface materials by spectrally and spatially. In the field of agriculture hyperspectral remote<br/>sensing offers study of species diversity, environmental stress, physiological features such as<br/>photosynthetic activity, plant productivity, canopy biochemistry, biomass and plant transpiration.<br/>Also for evaluation of vegetation stress, nutrient stress, moisture stress and crop growth models. In<br/>this study, Hyperion image datasets covering the area of Kokrajhar, Assam in L1 R format. The<br/>Hyperion images are preprocessed by the Hyperion tools provided as the extension to ENVI<br/>software. Atmospheric corrections are performed by QUAC and FLAASH. Feature extraction was<br/>performed to reduce the high dimensionality data into lower dimension via PCA transformation.<br/>Classification was done using three techniques, Spectral Angle Mapper, Maximum Likelihood<br/>Classification and Support Vector Machine based classification. In this three classification results,<br/>the SVM classification after PCA transformation gave more accurate results. From the classified<br/>results of SVM, the area for each land classes were estimated.<br/> <br/><br/>
502 ## - DISSERTATION NOTE
Degree type MSC GIS
Name of granting institution 2016-2018
Year degree granted INT
-- Mr. Radhakrishnan T
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element HYPERSPECTRAL REMOTE SENSING
9 (RLIN) 14542
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element HYPERION IMAGE
9 (RLIN) 14543
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element SUPPORT VECTOR MACHINE
9 (RLIN) 14544
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   19/07/2018   R-1446 19/07/2018 19/07/2018 Project Reports