30 April 2016 Simulation of the hyperspectral data from multispectral data using Python programming language
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Abstract
Multispectral remote sensing (MRS) sensors have proved their potential in acquiring and retrieving information of Land Use Land (LULC) Cover features in the past few decades. These MRS sensor generally acquire data within limited broad spectral bands i.e. ranging from 3 to 10 number of bands. The limited number of bands and broad spectral bandwidth in MRS sensors becomes a limitation in detailed LULC studies as it is not capable of distinguishing spectrally similar LULC features. On the counterpart, fascinating detailed information available in hyperspectral (HRS) data is spectrally over determined and able to distinguish spectrally similar material of the earth surface. But presently the availability of HRS sensors is limited. This is because of the requirement of sensitive detectors and large storage capability, which makes the acquisition and processing cumbersome and exorbitant. So, there arises a need to utilize the available MRS data for detailed LULC studies. Spectral reconstruction approach is one of the technique used for simulating hyperspectral data from available multispectral data. In the present study, spectral reconstruction approach is utilized for the simulation of hyperspectral data using EO-1 ALI multispectral data. The technique is implemented using python programming language which is open source in nature and possess support for advanced imaging processing libraries and utilities. Over all 70 bands have been simulated and validated using visual interpretation, statistical and classification approach.
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Varun Tiwari, Varun Tiwari, Vinay Kumar, Vinay Kumar, Kamal Pandey, Kamal Pandey, Rigved Ranade, Rigved Ranade, Shefali Agarwal, Shefali Agarwal, "Simulation of the hyperspectral data from multispectral data using Python programming language", Proc. SPIE 9880, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VI, 98800W (30 April 2016); doi: 10.1117/12.2223903; https://doi.org/10.1117/12.2223903
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