Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 991-995, 2014
https://doi.org/10.5194/isprsarchives-XL-8-991-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
28 Nov 2014
Hyperspectral Data for Land use/Land cover classification
D. Vijayan, G. Ravi Shankar, and T. Ravi Shankar National Remote Sensing Centre, Balanagar, Hyderabad, 500037, India
Keywords: Hyperion, Spectral Angle Mapper, Red Edge Inflection Point 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.
Conference paper (PDF, 1131 KB)


Citation: Vijayan, D., Ravi Shankar, G., and Ravi Shankar, T.: Hyperspectral Data for Land use/Land cover classification, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 991-995, https://doi.org/10.5194/isprsarchives-XL-8-991-2014, 2014.

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