I. INTRODUCTION

II. METHODOLOGY

III. EXPERIMENTAL RESULTS AND DISCUSS

IV. CONCLUSION

REFERENCES

This paper presents a Synthetic Aperture Radar (SAR) image classification algorithm based on multi-feature using Fruit Fly Optimization Algorithm (FOA) and Least Square Support Vector Machine (LS-SVM). First, pixel-based information derived from three elements of coherency matrix, six parameters obtained by H/α/A decomposition and Freeman decomposition techniques, and three polarimetric parameters including the total receive power (SPAN), pedestal height, and Radar Vegetation Index (RVI), as well as region-based information derived from eight texture parameters obtained by Grey Level Co-occurrence Matrix (GLCM) are combined to use as the features of land cover. Second, Kernel Principal Component Analysis (KPCA) is used to reduce the dimensionality of the multi-feature data derived from the integration of the pixel-based and region-based information. Third, LS-SVM is used as the classifier in this study due to its fast solving speed and desirable classification capability. Since the input parameters of LS-SVM significantly affect the classification performance, we employ FOA to obtain the optimized input parameters. Finally, the experiments on two fully polarimetric SAR images of various crops with a limited number of samples are implemented by the proposed method and other commonly used methods, respectively. The results show that the proposed method can attain better classification performances compared with other methods.

INTRODUCTION

Land cover information is important for land development and management. Land cover classification is also the first step in remote sensing of vital global parameters such as soil moisture. Remotely sensing data obtained from various sensors provides an economical way to characterize land cover information. Optical remote sensing is an effective approach but by itself is limited by weather conditions. Synthetic Aperture Radar (SAR), which can obtain information under different weather conditions, is therefore used for acquiring land cover information in various regions. Significant research aiming at land cover classification has been reported by many researchers. In the early years, most studies were developed based on single-polarization data [1], [2]. Since single-polarization data does not contain all the polarization information of ground objects, such methods were most likely to create confusion among similar ground objects and thereby were only suited for coarse classification [3]–[۵]. With the rapid development of the SAR techniques, many methods utilizing multi-polarization or full-polarization data were explored for attaining a better classification [6]–[۸]. The critical procedure for these methods is polarimetric decomposition, which provides a way to obtain the physical features of natural media. Many polarimetric decomposition methods have been explored by many researchers [9]–[۱۴].