Hyperspectral Data for Land use/Land cover classification

Abstract

ABSTRACT:An attempt has been made to compare the multispectral Resourcesat-2 LISS III and Hyperion image for the selected area at sub class level classes of major land use/ land cover. On-screen interpretation of LISS III (resolution 23.5 m) was compared with Spectral Angle Mapping (SAM) classification of Hyperion (resolution 30m). Results of the preliminary interpretation of both images showed that features like fallow, built up and wasteland classes in Hyperion image are clearer than LISS-III and Hyperion is comparable with any high resolution data. Even canopy types of vegetation classes, aquatic vegetation and aquatic systems are distinct in Hyperion data. Accuracy assessment of SAM classification of Hyperion compared with the common classification systems followed for LISS III there was no much significant difference between the two. However, more number of vegetation classes could be classified in SAM. There is a misinterpretation of built up and fallow classes in SAM. The advantages of Hyperion over visual interpretation are the differentiation of the type of crop canopy and also crop stage could be confirmed with the spectral signature. The Red edge phenomenon was found for different canopy type of the study area and it clearly differentiated the stage of vegetation, which was verified with high resolution image. Hyperion image for a specific area is on par with high resolution data along with LISS III data.

Comprehensive review on land use/land cover change classification in remote sensing

“…9,19,25,29,30,34,46,47,51,53,54,56,[61][62][63]67,80,82 Frequently used atmospheric correction methods are Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), FLAASH, Apparent Reflectance Model (ARM) and the F mask method. [7][8][9]12,15,[18][19][20]23,28,29,33,35,37,40,41,45,49,[61][62][63]66,67,70,74,75,79 Geometric corrections include Orthorectification, Geo-referencing, Image Registration, ASCII Coordinate Conversion and Resampling. 1,7,13,16,[22]…”

“…Correction [7][8][9]12,15,[18][19][20]23,28,29,33,35,37,40,41,45,49,[61][62][63]66,67,70,74,75,79 This method helps to correct the atmospheric effects on the reflectance values of the satellite images.…”

“…SAM Classification 41,52,62,63,66 The LU/LC classes in the satellite image are identified based on calculation of the spectral angle.…”

“…The overall accuracy and the kappa coefficient are computed to validate the LU/LC classified result. 1,3,4,[7][8][9][10][12][13][14][15][16][17]19,20,22,23,[25][26][27][28][53][54][55][56][57]62,63,[66][67][68]70,72,73,80…”

Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art

“…All these enhance the information that was utilized for land cover mapping with ease and accuracy than individual data. (Kanellopoulos et al 1992;Lee and Lathrop 2006;Tan KC et al 2010;Salehi et al 2013;Singh S et al 2018) Hyperspectral (Thenkabail et al 2004;Pal 2006;Tan Q and Wang 2007;Liu and Li 2013;Hegde et al 2014;Vijayan et al 2014;Pandey et al 2018) SAR (Henderson F 1975;Henderson FM and Xia 1997;Saatchi et al 2000;Simard et al 2000;da Costa Freitas et al 2008;Werner et al 2014;Clerici et al 2017;Hagensieker et al 2017;Spies et al 2017) 3 Multi-Temporal datasets Different time-period data (Roberts et al 2002;Engdahl and Hyyppa 2003;Rogan et al 2003;Budreski et al 2007;Pandey et al 2012;Pandey et al 2013;Sexton et al 2013;Campbell et al 2015;Feng et al 2015;Bai et al 2017…”