Exploitation of TerraSAR-X Data for Land use/Land Cover Analysis Using Object-Oriented Classification Approach in the African Sahel Area, Sudan

Abstract

Recently, object-oriented classification techniques based on image segmentation approaches are being studied using high-resolution satellite images to extract various thematic information. In this study different types of land use/land cover (LULC) types were analysed by employing object-oriented classification approach to dual TerraSAR-X images (HH and HV polarisation) at African Sahel. For that purpose, multi-resolution segmentation (MRS) of the Definiens software was used for creating the image objects. Using the feature space optimisation (FSO) tool the attributes of the TerraSAR-X image were optimised in order to obtain the best separability among classes for the LULC mapping. The backscattering coefficients (BSC) for some classes were observed to be different for HH and HV polarisations. The best separation distance of the tested spectral, shape and textural features showed different variations among the discriminated LULC classes. An overall accuracy of 84 % with a kappa value 0.82 was resulted from the classification scheme, while accuracy differences among the classes were kept minimal. Finally, the results highlighted the importance of a combine use of TerraSAR-X data and object-oriented classification approaches as a useful source of information and technique for LULC analysis in the African Sahel drylands.

A Case Study of the Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery

“…The significant impacts of soil salinity on the soil-water-plant system can reduce the nutrient absorption and lead to a considerable decrease of crop productivity [2,3]. Remote sensing has been shown to be a particularly valuable tool for monitoring soil conditions frequently and spatially [4][5][6]. The presence of salts can be detected directly on bare soils with salt crust via the variation of spectral reflectance, and the spectral behaviour of salt has been studied in detail [7,8].…”

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

“…[3][4][5][6][7] In processing high-resolution remote sensing images, numerous classification algorithms, such as the object-oriented approach, [8][9][10] based on the classification of a support vector machine (SVM) [11][12][13] and Markov random fields (MRF) [14][15][16][17][18] are being developed. Local features [19][20][21][22][23] have been successfully applied to image retrieval, semantic segmentation, and scene understanding.…”

Comparison Between Spectral, Spatial and Polarimetric Classification of Urban and Periurban Landcover Using Temporal Sentinel – 1 Images

“…To overcome this difficulty, researchers started utilizing polarimetric SAR (PolSAR) data to study landuse/landcover information (Du & Lee, 1996;Freitas et al, 2008;Pierce, Ulaby, Sarabandi, & Dobson, 1994). The results showed that PolSAR measurements achieved much better classification results than single polarization SAR (Biro, Pradhan, Sulieman, & Buchroithner, 2013;Qi, Yeh, Li, & Zhang, 2015;. PolSAR data were mainly used for mapping landuse/landcover in river catchments (Ahmed, Garg, Singh, & Raman, 2014) and flood affected areas (Manavalan, Rao, Krishna Mohan, Venkataraman, & Chattopadhyay, 2013).…”

“…Several methods were implemented to identify urban landuse/landcover classes. Some of these methods are supervised classification from backscatter and coherence (Parihar, Das, Rathore, Nathawat, & Mohan, 2014), unsupervised classification (Ince, 2010), object-oriented image analysis, change vector analysis, post-classification comparison (Biro et al, 2013;Qi et al, 2015), change detection matrix (Lê, Atto, Trouvé, Solikhin, & Pinel, 2015), polarimetric decomposition, Pol-SAR interferometry, and decision tree algorithms (Qi, Yeh, Li, & Lin, 2012). Fusion of optical and SAR images also proved to be a useful method in urban landuse/landcover classification.…”