Fusun Balik Sanli

Application of Support Vector Machines for Landuse Classification Using High-Resolution RapidEye Images: A Sensitivity Analysis

Abstract The classification accuracy of remotely sensed data and its sensitivity to classification algorithms have a critical importance for the geospatial community, as classified images provide the base layers for many applications and models. Support Vector Machines (SVMs), a non-parametric statistical learning method that has recently been used in numerous applications in image processing. The SVMs need user-defined parameters and each parameter has different impact on kernels hence the classification accuracy of SVMs is based upon the choice of the parameters and kernels. The objective of this study is to investigate the sensitivity of SVM architecture including internal parameters and kernel types on landuse classification accuracy of RapidEye imagery for the study area in Turkey. Four types of kernels (linear, polynomial, radial basis function, and sigmoid) were used for the SVM classification. A total of 63 different models were developed and implemented for sensitivity analysis of SVM architecture. The traditional Maximum Likelihood Classification (MLC) method was also performed for comparison. The classification accuracies of the best model for each kernel type and MLC are 85.63%, 83.94%, 83.94%, 83.82% and 81.64% for polynomial, linear, radial basis function, sigmoid kernels and MLC, respectively. The results suggest that the choice of model parameters and kernel types play an important role on SVMs classification accuracy. Best model of polynomial kernel outperformed all SVMs models and gave the highest classification accuracy of 85.63% with RapidEye imagery.

Enhancing land use classification with fusing dual-polarized TerraSAR-X and multispectral RapidEye data

Abstract. The contribution of dual-polarized synthetic aperture radar (SAR) to optical data for the accuracy of land use classification is investigated. For this purpose, different image fusion algorithms are implemented to achieve spatially improved images while preserving the spectral information. To compare the performance of the fusion techniques, both the microwave X-band dual-polarized TerraSAR-X data and the multispectral (MS) optical image RapidEye data are used. Our test site, Gediz Basin, covers both agricultural fields and artificial structures. Before the classification phase, four data fusion approaches: (1) adjustable SAR-MS fusion, (2) Ehlers fusion, (3) high-pass filtering, and (4) Bayesian data fusion are applied. The quality of the fused images was evaluated with statistical analyses. In this respect, several methods are performed for quality assessments. Then the classification performances of the fused images are also investigated using the support vector machines as a kernel-based method, the random forests as an ensemble learning method, the fundamental k-nearest neighbor, and the maximum likelihood classifier methods comparatively. Experiments provide promising results for the fusion of dual polarimetric SAR data and optical data in land use/cover mapping.

Evaluation of image fusion methods using PALSAR, RADARSAT-1 and SPOT images for land use/ land cover classification

This research aimed to explore the fusion of multispectral optical SPOT data with microwave L-band ALOS PALSAR and C-band RADARSAT-1 data for a detailed land use/cover mapping to find out the individual contributions of different wavelengths. Many fusion approaches have been implemented and analyzed for various applications using different remote sensing images. However, the fusion methods have conflict in the context of land use/cover (LULC) mapping using optical and synthetic aperture radar (SAR) images together. In this research two SAR images ALOS PALSAR and RADARSAT-1 were fused with SPOT data. Although, both SAR data were gathered in same polarization, and had same ground resolution, they differ in wavelengths. As different data fusion methods, intensity hue saturation (IHS), principal component analysis, discrete wavelet transformation, high pass frequency (HPF), and Ehlers, were performed and compared. For the quality analyses, visual interpretation was applied as a qualitative analysis, and spectral quality metrics of the fused images, such as correlation coefficient (CC) and universal image quality index (UIQI) were applied as a quantitative analysis. Furthermore, multispectral SPOT image and SAR fused images were classified with Maximum Likelihood Classification (MLC) method for the evaluation of their efficiencies. Ehlers gave the best score in the quality analysis and for the accuracy of LULC on LULC mapping of PALSAR and RADARSAT images. The results showed that the HPF method is in the second place with an increased thematic mapping accuracy. IHS had the worse results in all analyses. Overall, it is indicated that Ehlers method is a powerful technique to improve the LULC classification.

Land use and cover classification of Sentinel-IA SAR imagery: A case study of Istanbul

In this study, Sentinel-1A SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and K-NN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest classification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-1A imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy.

Combining Landsat and ALOS data for land cover mapping

In this study, L-band ALOS PALSAR radar satellite image and Landsat TM optical satellite image were used to investigate the contribution of radar satellite image to optical satellite image for land cover mapping. Dual-polarimetric data of ALOS satellite and also normalized difference vegetation index (NDVl) generated from Landsat image were used for the analysis. In addition, different classification techniques were taken into consideration and forest dominated land cover maps were produced and the results were compared. Random Forest (RF), k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM) approaches were applied as image classification techniques. While the best result among the methods is DVM, the data set in which combined data are used gives the best general accuracy result.

Fusion and classification of synthetic aparture radar and multispectral sattellite data

In this study, synthetic aperture radar (SAR) and multispectral data are fused with different methods in order to observe the effect of fusion methods on the accuracy of different classification techniques. At the same time, different polarizations of SAR data are included in fusion process and results are examined. The fusion methods that are used in this study are Brovey Color Normalized, Hue Saturation Value (HSV), Gram - Schmidt (GS) Spectral Sharpening and Principal Components (PC) Spectral Sharpening. Fused images are classified using k-nearest neighbor, support vector machine and radial based function neural network. The study area is chosen on Menemen Plain, which contains agricultural lands, and it is located in İzmir. Multispectral RapidEye satellite image and TerraSAR-X radar data are used for the analysis. Achieved results were presented in the tables. The highest accuracy is achieved by K-NN classification of TerraSAR-X and VH fusion with GS method as 95.74%.