The temporal and spatial variation of precipitable water (PWV) has an important effect on the forecast of short-time severe weather such as rainstorm and hail. In this paper, cloud mask is used to remove cloudy data, and then inverse distance weighting (D-IDW) algorithm of DEM with elevation information is used to process study area 1(Visalia, California, USA), Study area 2(Los Angeles, California, USA) and study Area 3(Bishkek, Kyrgyzstan). The problem that MODIS cannot be used in cloud areas is solved. Finally, the improved fusion model method is used to fuse MODIS data with ERA5 data to obtain PWV with high spatial resolution and high precision. The fusion of PWV image and ERA5-PWV image has a good similarity in the spatial distribution changes of study area 1 and study area 3, which have a large elevation fluctuation (elevation change is greater than 2 km). The Global Navigation Satellite System (GNSS)-PWV was used to verify the accuracy of the fusion data. The minimum deviation was found in December in the study area 1, with an average deviation of 1.79 mm. Study area 2 was the smallest in December, with a mean deviation of 1.91mm. Study area 3 had the smallest deviation in December, with a mean deviation of 1.52mm.
To solve the problem that the H/α-Wishart unsupervised classification algorithm can generate only inflexible clusters due to arbitrarily fixed zone boundaries in the clustering processing, a refined fuzzy logic based classification scheme called the H/α-Wishart fuzzy clustering algorithm is proposed in this paper. A fuzzy membership function was developed for the degree of pixels belonging to each class instead of an arbitrary boundary. To devise a unified fuzzy function, a normalized Wishart distance is proposed during the clustering step in the new algorithm. Then the degree of membership is computed to implement fuzzy clustering. After an iterative procedure, the algorithm yields a classification result. The new classification scheme is applied to two L-band polarimetric synthetic aperture radar (PolSAR) images and an X-band high-resolution PolSAR image of a field in LingShui, Hainan Province, China. Experimental results show that the classification precision of the refined algorithm is greater than that of the H/α-Wishart algorithm and that the refined algorithm performs well in differentiating shadows and water areas.
A method combined Kernel Principal Component Analysis (KPCA) with BP neural network is proposed for
multispectral remote sensing image classification in this paper. Firstly, the KPCA transformation including Gaussian
KPCA and polynomial KPCA is carried out to get the former three uncorrelated bands containing most information of
the TM images with seven bands. Secondly, BP neural network classification is executed using the three bands data after
KPCA transformation. For testifying, both the classical PCA and the KPCA are applied to the multispectral Landsat TM
data for feature extraction. The results demonstrate that the method proposed in this paper can improve the classification
accuracy compared with that of principal component analysis (PCA) and BP neural network.
The aim of the filter processing to LIDAR dataset is to divide the dataset into ground points and non-ground points. So, the filtering of LIDAR dataset is a crucial step to obtain the DEM with high precision. Over the last few years, some algorithms have been developed to filter LIDAR data. This paper studies three filtering algorithms that are used in common at present, brings about some improvement to the MLS filtering algorithm which is one of the three filtering algorithms and gives two datasets as experimental data in order to compare and analyze the filtering results.
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