In order to accurately identify large-scale sugarcane plantations, pre-processed Sentinel-2A remote sensing imagery data from three different time periods were used in this study. The vegetation index features NDVI, BI2 and S2REP were calculated and the optimal texture feature, homogeneity, was selected using principal component analysis and the grey level co-occurrence matrix. A random forest algorithm was then used to construct identification models with different combinations of features, including single time period and single feature, single time period and multiple features, multiple time periods and single feature, and multiple time periods and multiple features. The experiment was conducted in Nansha District, Guangzhou, and the results showed that the fusion of NDVI, BI2, homogeneity and temporal features provided the best identification results for sugarcane planting areas, with clear object boundaries and effective control of salt and pepper noise in the images. The overall accuracy reached 0.974 and the kappa coefficient reached 0.931. In the result validation, the identification accuracy for Nansha District, Guangzhou was 81.6%. The experimental errors were also analysed and the method was found to be suitable for determining the area under sugarcane cultivation in the Pearl River Delta region, providing valuable reference information for the agricultural sector in monitoring the growth of sugarcane, estimating yields and forecasting prices.
With the emergence of very high-resolution airborne synthetic aperture radar systems, it is necessary to reinvestigate these proposed methods with respect to their despeckling performances. As for the very high resolution polarimetric synthetic aperture radar (PolSAR) data, the presumption that the resolution cell is much larger than the radar wavelength becomes ineffective. Therefore, some classic and new filters are thoroughly reviewed. For the evaluation of speckle filters, both indicators for polarimetric information and spatial information are listed. The absolute relative bias is introduced, with the purpose of measuring the filtering performance concerning the indicators for polarimetric information. Moreover, the ratio of half power point width is employed to quantitatively assess the degree of point target preservation. A series of experiments are carried out based on the real PolSAR imagery which is obtained from an uninhabited aerial vehicle synthetic aperture radar system. It can be concluded that existing filters can only attain good performance with reference to part of the indicators. As regards very high-resolution PolSAR imagery, it is necessary to conceive more apposite new filters or make improved versions of the existing filters.
Speckle filtering seems to be a never-ending topic for polarimetric synthetic aperture radar imagery processing. Constantly emerging literatures demonstrate that this issue deserves further research effort, especially in the context of much more high spatial resolution. A comparative study will be performed in this paper for recently proposed method such as non-local SAR speckle filtering, Extended Sigma filter proposed by Lee, non-local means filter, Bilateral filter, and so on. Their performance on spatial details preserving and polarimetric properties preserving should be measured thoroughly. Further more the computing performance on large-scale dataset should also be measured.
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