Application of geographic image cognition approach in land type classification using Hyperion image: A case study in China

Wang, Jing; Chen, Yongqi; He, Tao; Lv, Chunyan; Liu, Aixia

doi:10.1016/j.jag.2009.06.003

Cited by 25 publications

(11 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hyperion imagery has been the focus of LULC classification until today (Xu and Gong 2007, Pignatti et al 2009, Wang et al 2009, White et al 2010. Different pixelbased classification algorithms have been combined with Hyperion data in various applications requiring image classification (Galvao et al 2005, Wang et al 2009).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data

Elatawneh

Kalaitzidis

Petropoulos

et al. 2012

International Journal of Digital Earth

View full text Add to dashboard Cite

Information on Earth's land surface cover is commonly obtained through digital image analysis of data acquired from remote sensing sensors. In this study, we evaluated the use of diverse classification techniques in discriminating land use/cover types in a typical Mediterranean setting using Hyperion imagery. For this purpose, the spectral angle mapper (SAM), the object-based and the non-linear spectral unmixing based on artificial neural networks (ANNs) techniques were applied. A further objective had been to investigate the effect of two approaches for training sites selection in the SAM classification, namely of the pixel purity index (PPI) and of the direct selection of training points from the Hyperion imagery assisted by a QuickBird imagery and field-based training sites. Object-based classification outperformed the other techniques with an overall accuracy of 83%. Sub-pixel classification based on the ANN showed an overall accuracy of 52%, very close to that of SAM (48%). SAM applied using the training sites selected directly from the Hyperion imagery supported by the QuickBird image and the field visits returned an increase accuracy by 16%. Yet, all techniques appeared to suffer from the relatively low spatial resolution of the Hyperion imagery, which affected the spectral separation among the land use/cover classes.Peer reviewe

show abstract

Section: Introductionmentioning

confidence: 99%

“…Different pixelbased classification algorithms have been combined with Hyperion data in various applications requiring image classification (Galvao et al 2005, Wang et al 2009). Various spectral unmixing classification techniques have also been combined with Hyperion data in land classification studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data

Elatawneh

Kalaitzidis

Petropoulos

et al. 2012

International Journal of Digital Earth

View full text Add to dashboard Cite

show abstract

“…[12][13][14][15][16][17][18] In particular, the Hyperion sensors used for land use/cover classification can capture 256 spectra, each with 242 narrow electromagnetic bands, including visible and shortwave-infrared. [19][20][21][22][23][24][25][26][27] However, few studies have examined the use of previous Landsat multispectral satellite series in land use/cover mapping applications. 17,18,28,29 In addition to the different sensors, the classification of remote sensing imagery can employ both parametric and nonparametric classifiers, for example, the spectral angle mapper (SAM) 30,31 and the support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery

Pervez

Uddin

Khan

et al. 2016

J. Appl. Remote Sens

View full text Add to dashboard Cite

, "Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery," J. Appl. Remote Sens. 10(2), 026004 (2016), doi: 10.1117/1.JRS.10.026004. Abstract. Until recently, Landsat technology has suffered from low signal-to-noise ratio (SNR) and comparatively poor radiometric resolution, which resulted in limited application for inland water and land use/cover mapping. The new generation of Landsat, the Landsat Data Continuity Mission carrying the Operational Land Imager (OLI), has improved SNR and high radiometric resolution. This study evaluated the utility of orthoimagery from OLI in comparison with the Advanced Land Imager (ALI) and hyperspectral Hyperion (after preprocessing) with respect to spectral profiling of classes, land use/cover classification, classification accuracy assessment, classifier selection, study area selection, and other applications. For each data source, the support vector machine (SVM) model outperformed the spectral angle mapper (SAM) classifier in terms of class discrimination accuracy (i.e., water, built-up area, mixed forest, shrub, and bare soil). Using the SVM classifier, Hyperion hyperspectral orthoimagery achieved higher overall accuracy than OLI and ALI. However, OLI outperformed both hyperspectral Hyperion and multispectral ALI using the SAM classifier, and with the SVM classifier outperformed ALI in terms of overall accuracy and individual classes. The results show that the new generation of Landsat achieved higher accuracies in mapping compared with the previous Landsat multispectral satellite series. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 UnportedLicense. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

“…In supervised classification, estimates are derived from the training samples which include number of classes be specified in advance (Plaza, 2005;Plaza, 2009;Small, C. 2001). Using Hyperion hyperspectral imagery, accuracy of different classification approaches for land use mapping is rare in the literature (Du, P.,2010;Pignatti, S., 2009;Walsh, S. J., 2008;Wang, J., 2010).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Hyperion Imagery Analysis and Its Application Using Spectral Analysis

Pervez

Khan

Valiuddin³

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.

show abstract

Application of geographic image cognition approach in land type classification using Hyperion image: A case study in China

Cited by 25 publications

References 37 publications

Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data

Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data

Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery

Hyperspectral Hyperion Imagery Analysis and Its Application Using Spectral Analysis

Contact Info

Product

Resources

About