Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping

Classification of Tundra Vegetation in the Krkonoše Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

“…However, the overall accuracy and also results for most of individual categories in the case of APEX data are comparable and it can be concluded that higher spatial and spectral resolutions of AISA Dual data brought only moderate improvement. Similarly to many older and recent studies [for example Camps-Valls et al, 2004;Pal and Mather 2005;Petropoulos et al, 2012] the best results for the both types of hyperspectral data were achieved using SVM classification algorithm. As for Sentinel-2A data, especially in the case of simplified legend, NN and MLC methods achieved better results than SVM.…”

“…All the algorithms were performed using ENVI 5.3 software and are described in detail in the following text. Parametrization of the classification methods was in general performed in accordance with many analogue studies (Pal and Mather [2005]; Petropoulos et al [2012], Zhou and Yang [2008]) based on a set of tests. We run all the classification algorithms many times, different number of bands and different settings of parameters were tested (always only one parameter was gradually changing while other parameters stayed the same) and several final sets of parameters' combinations that produced the best (or very good) classification results for each classification method are introduced in the chapter Results (see also Tabs.…”

“…Increasing value of the penalty parameter increases also the cost of misclassifying points and causes ENVI to create a more accurate model that may not generalize well (https://www.harris geospatial.com/docs/supportvectormachine.html). After the series of tests evaluated by classification accuracy results the default parameters were used similarly like in Petropoulos et al [2012]. It means that gamma kernel was set up as an inverse number of the number of bands in the input image (e.g.…”

“…Training samples used for construction of hyperplane are called support vectors. These lie on the margin of categories to be classified and are extracted automatically by the algorithm [Camps-Valls et al, 2004;Jones and Vaughan, 2010;Mountrakis et al, 2011;Petropoulos et al, 2012].…”

Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests

“…A variety of classification approaches has been applied to remotely sensed hyperspectral data [Lu and Weng, 2007]: Spectral Angle Mapper [Vyas et al, 2011], Linear Discriminant Analysis [Clark et al, 2005], Decision Tree Classifier [Lawrence et al, 2004], Artificial Neural Networks [Erbek et al, 2004], Support Vector Machine [Dalponte et al, 2009] and Random Forest [Chan and Palinckx, 2008] are some of the advanced methods for hyperspectral data classification. Recently, Hyperion imagery data was used to map LULC in the Mediterranean context [Pignatti et al, 2009;Petropoulos et al, 2012]. Despite their interesting results, the main limitation of these experimental studies is due to the little training and validation subsets, entailing the inability to extend their findings on wider areas.…”