Browsing by Subject "multispectral"

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Access status: Open Access ,
Forest community mapping using hyperspectral (CHRIS/PROBA) and Sentinel-2 multispectral images
(Wydawnictwa AGH, 2022) Głowienka, Ewa; Zembol, Nicole
The possibility to use hyperspectral images (CHRIS/PROBA) and multispectral images (Sentinel-2) in the classification of forest communities is assessed in this article. The pre-processing of CHRIS/PROBA image included: noise reduction, radiometric correction, atmospheric correction, geometric correction. Due to MNF transformation the number of the hyperspectral image channels was reduced (to 10 channels) and smiling errors were removed. Sentinel-2 image (level 2A) did not require pre-processing. Three tree genera occurring in the study area were selected for the classification: pine (Pinus), alder (Alnus) and birch (Betula). Image classification was carried out with three methods: SAM (Spectral Angle Mapper), MTMF (Mixture Tuned Matched Filtering), SVM (Support Vector Machine). For the CHRIS/PROBA image, the algorithm SVM turned out to be the best. Its overall accuracy (OA) was 72%. The poorest result (OA = 52%) was for the MTMF classifier. In the classification of Sentinel-2 multispectral image the best result was for the MTMF method: OA = 82%, kappa coefficient 0.7. For other methods, the overall accuracy exceeded 65%. Among the classified genera, the highest producer's accuracy was obtained for pine (PA = 96%), and the broad-leaf genera: alder and birch had PA ranging from 42% to 85%.
Access status: Restricted ,
Geological mapping of North Wazirstan, Pakistan using remotely sensed images
(Data obrony: 2017-10-06) Nawaz, Adil
Wydział Geologii, Geofizyki i Ochrony Środowiska
Digitally enhanced OLI Landsat 8 images were applied for mapping of North Waziristan Pakistan. The territory is rough and without rich vegetation; the exposure of the Waziristan ophiolite, related sedimentary lithologies and inaccessibility to the area made the utilization of Landsat information helpful in this investigation. In the remote sensing investigation, Landsat 8 OLI data were used to make band ratios, band combinations, principal component and image classification methods. Multispectral images were prepared and investigated for this study. On the basis of the image classification techniques; unsupervised classification, five principle lithological units are marked which are giving satisfactory results (about 63.07 %.) when compared with referenced geological map using confusion matrix analysis. The outcomes are very satisfied and need to examine about the utility and confinements of remote sensing strategy on the investigation zone. Further to confirm the results of unsupervised classification, extra investigations might be helpful. As a results, issues confronted during the classification must be considered into all general accuracy.