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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Texture and Intensity Based Land Cover Classification in Germany from Multi-Orbit & Multi-Temporal Sentinel-L Images
摘要： Land cover information is vital for ecosystem management, to find biodiversity indicators and for sustainable development. The launch of the Sentinel-1 satellites provide large amounts of Synthetic Aperture Radar (SAR) data that can be used for the extraction and classification of land cover. This study presents a preliminary method for land cover classification using SAR amplitude and textural features and by combining multi-temporal images from ascending and descending orbits. The texture parameters contrast, entropy, homogeneity and variance were investigated. The rules of the SAR-LC classifier, designed and implemented at the Federal Agency for Cartography and Geodesy in Frankfurt, were optimised to include textual information for processing the multi-temporal SAR images. The results for land cover classification of images from 2017 for the area around Berlin in Germany are reported along with their classification efficiencies.
关键词： Land cover classification,Germany,Copernicus,Sentinel-1,object based classification,texture
更新于2025-09-23 15:21:21
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Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery
摘要： Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.
关键词： accuracy,infrared,segmentation,object-based classification,orthophoto,high resolution imagery,land cover
更新于2025-09-19 17:15:36
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Use of artificial neural networks and geographic objects for classifying remote sensing imagery
摘要： The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1) to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2) to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3) to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.
关键词： time series,object-based classification,image segmentation
更新于2025-09-09 09:28:46