<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Senf, Cornelius</style></author><author><style face="normal" font="default" size="100%">Leitão, Pedro J.</style></author><author><style face="normal" font="default" size="100%">Pflugmacher, Dirk</style></author><author><style face="normal" font="default" size="100%">van der Linden, Sebastian</style></author><author><style face="normal" font="default" size="100%">Hostert, Patrick</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery</style></title><secondary-title><style face="normal" font="default" size="100%">Remote Sensing of Environment</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Landsat</style></keyword><keyword><style  face="normal" font="default" size="100%">Mediterranean</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenology</style></keyword><keyword><style  face="normal" font="default" size="100%">Pseudo-steppe</style></keyword><keyword><style  face="normal" font="default" size="100%">STARFM</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">156</style></volume><pages><style face="normal" font="default" size="100%">527-536</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Low-intensity farming systems are of great importance for biodiversity in Europe, but they are often affected by soil degradation or economic pressure, leading to either land abandonment or intensification of agriculture. These changes in land use influence local biodiversity patterns and require annual monitoring of land cover. To accuratelymap landcover in such spatio-temporal complex landscapes, it isimportant to capture their phenolog- ical dynamics andfine spatial heterogeneity.Multi-seasonal analyses using optical sensorswith amediumspatial resolution from10 to 60m(e.g. Landsat) have been used for this task, but data availability can be scarce due to cloud cover, sub-optimal acquisition schedules and data archive access restrictions. Combining coarse spatial res- olution data fromtheMODerate-resolution Imaging Spectroradiometer(MODIS) and Landsat provides opportu- nities to close these gaps by simulating Landsat-like images atMODIS temporal resolution. In this study,we test whether and by what degree land cover maps of complex Mediterranean landscapes improve by integrating multi-seasonal Landsat imagery, as well aswhether STARFM-simulated imagery can be usedwhenever original multi-seasonal Landsat observations are unavailable. Therefore, we develop different classification scenarios based on seasonally varying data availability and based on original and simulated Landsat data. Results show that multi-seasonal Landsat data from spring and early autumn are crucial for achieving satisfying mapping accuracies (overall accuracy 74.5%). Using synthetic Landsat imagery increases classification accuracy compared to using single-date Landsat data, but accuracieswere never as good as a classification based on original data.We conclude thatmulti-seasonal data is essential for mapping complex Mediterranean landscapes and that STARFM can be used to compensate for missing Landsat observations. However, if Landsat data availability is sufficient to cover all phenologically important dates, we suggest relying solely on Landsat.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">El Maia, H.</style></author><author><style face="normal" font="default" size="100%">Hammouch, A.</style></author><author><style face="normal" font="default" size="100%">Aboutajdine, D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Color-texture analysis by mutual information for multispectral image classification</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">co-occurrence matrix</style></keyword><keyword><style  face="normal" font="default" size="100%">feature extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">forest areas</style></keyword><keyword><style  face="normal" font="default" size="100%">hybrid color texture space</style></keyword><keyword><style  face="normal" font="default" size="100%">image analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Image color analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">image colour analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Image databases</style></keyword><keyword><style  face="normal" font="default" size="100%">image texture</style></keyword><keyword><style  face="normal" font="default" size="100%">Information analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Morocco</style></keyword><keyword><style  face="normal" font="default" size="100%">multispectral image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Multispectral imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">mutual information</style></keyword><keyword><style  face="normal" font="default" size="100%">Rabat</style></keyword><keyword><style  face="normal" font="default" size="100%">RGB color space</style></keyword><keyword><style  face="normal" font="default" size="100%">Spatial databases</style></keyword><keyword><style  face="normal" font="default" size="100%">SPOT HRV (XS)</style></keyword><keyword><style  face="normal" font="default" size="100%">Support vector machine classification</style></keyword><keyword><style  face="normal" font="default" size="100%">support vector machines</style></keyword><keyword><style  face="normal" font="default" size="100%">support vectors machine (citation)</style></keyword><keyword><style  face="normal" font="default" size="100%">VisTex database</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009///</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, we propose a new approach for the construction of a hybrid color-texture space by using mutual information. Feature extraction is done by the co-occurrence matrix with SVM (support vectors machine) as a classifier. We apply our approach to the VisTex database and to the classification of a SPOT HRV (XS) image representing two forest areas in the region of Rabat in Morocco. We compare the result of classification obtained in this hybrid space with the one in the RGB color space.</style></abstract><notes><style face="normal" font="default" size="100%">The following values have no corresponding Zotero field:&lt;br/&gt;secondary-title: Communications, Computers and Signal Processing, 2009. PacRim 2009. IEEE Pacific Rim Conference on&lt;br/&gt;periodical: Communications, Computers and Signal Processing, 2009. PacRim 2009. IEEE Pacific Rim Conference on&lt;br/&gt;pages: 359-364&lt;br/&gt;isbn: VO -&lt;br/&gt;electronic-resource-num: 10.1109/PACRIM.2009.5291344</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>31</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">El Maia, H</style></author><author><style face="normal" font="default" size="100%">Hammouch, A</style></author><author><style face="normal" font="default" size="100%">Aboutajdine, D</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Color-texture analysis by mutual information for multispectral image classification</style></title><secondary-title><style face="normal" font="default" size="100%">Communications, Computers and Signal Processing, 2009. PacRim 2009. IEEE Pacific Rim Conference on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">co-occurrence matrix</style></keyword><keyword><style  face="normal" font="default" size="100%">feature extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">forest areas</style></keyword><keyword><style  face="normal" font="default" size="100%">hybrid color texture space</style></keyword><keyword><style  face="normal" font="default" size="100%">image analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Image color analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">image colour analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Image databases</style></keyword><keyword><style  face="normal" font="default" size="100%">image texture</style></keyword><keyword><style  face="normal" font="default" size="100%">Information analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Morocco</style></keyword><keyword><style  face="normal" font="default" size="100%">multispectral image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Multispectral imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">mutual information</style></keyword><keyword><style  face="normal" font="default" size="100%">Rabat</style></keyword><keyword><style  face="normal" font="default" size="100%">RGB color space</style></keyword><keyword><style  face="normal" font="default" size="100%">Spatial databases</style></keyword><keyword><style  face="normal" font="default" size="100%">SPOT HRV (XS)</style></keyword><keyword><style  face="normal" font="default" size="100%">Support vector machine classification</style></keyword><keyword><style  face="normal" font="default" size="100%">support vector machines</style></keyword><keyword><style  face="normal" font="default" size="100%">support vectors machine (citation)</style></keyword><keyword><style  face="normal" font="default" size="100%">VisTex database</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">359-364</style></pages><isbn><style face="normal" font="default" size="100%">VO -</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, we propose a new approach for the construction of a hybrid color-texture space by using mutual information. Feature extraction is done by the co-occurrence matrix with SVM (support vectors machine) as a classifier. We apply our approach to the VisTex database and to the classification of a SPOT HRV (XS) image representing two forest areas in the region of Rabat in Morocco. We compare the result of classification obtained in this hybrid space with the one in the RGB color space.</style></abstract></record></records></xml>