Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Hyperspectral Data for Land use/Land cover classification

View through CrossRef
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.
Title: Hyperspectral Data for Land use/Land cover classification
Description:
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.

Related Results

Design and Development of Open Source Software Solution for Hyperspectral Data Simulation: Hydas.
Design and Development of Open Source Software Solution for Hyperspectral Data Simulation: Hydas.
Abstract Multispectral remote sensing data is available with broad spectral bands in the wavelength range of Visible-NIR-SWIR, and finds its applications in assessment of L...
Mapping Mineralogical Distributions on Mars with Unsupervised Machine Learning
Mapping Mineralogical Distributions on Mars with Unsupervised Machine Learning
Abstract Knowledge of the constituents of the Martian surface and their distributions over the planet informs us about Mars’ geomorphological formation and evolutionary h...
Current Advances in Hyperspectral Face Recognition
Current Advances in Hyperspectral Face Recognition
Hyperspectral imaging systems are well established, for satellite, remote sensing and geosciences applications. Recently, the reduction in the cost of hyperspectral sensors and inc...
Current Advances in Hyperspectral Face Recognition
Current Advances in Hyperspectral Face Recognition
Hyperspectral imaging systems are well established, for satellite, remote sensing and geosciences applications. Recently, the reduction in the cost of hyperspectral sensors and inc...
Learned Hyperspectral Compression Using a Student’s T Hyperprior
Learned Hyperspectral Compression Using a Student’s T Hyperprior
Hyperspectral compression is one of the most common techniques in hyperspectral image processing. Most recent learned image compression methods have exhibited excellent rate-distor...
Cover Crop Response to Late‐Season Planting and Nitrogen Application
Cover Crop Response to Late‐Season Planting and Nitrogen Application
Cover crops aid in reducing precipitation runoff, soil erosion, and N losses in highly sloped, mountainous regions. Corn (Zea mays L.) producers in states with late spring warmup a...
Hyperspectral imaging and artificial neural networks for oil spills automatic detection
Hyperspectral imaging and artificial neural networks for oil spills automatic detection
Abstract The detection of hydrocarbon spills in marine environments is a critical challenge due to their severe impact on aquatic ecosystems. Hyperspectral imagin...

Back to Top