Sample quality is the key to automated cloud detection from regional remote sensing images, and scale is one of the major impediments to sample quality control. In this paper, we select the southwest mountainous area in China, which is fragmented, cloudy, and rainy, as the study area. We proposed a method for constructing a cloud detection dataset based on the idea of downscaling and the spectral characteristics of vegetation. Finally, we validated the dataset by the U-Net+ deep learning model. The experimental results show that the cloud detection accuracy reaches 95.11% when using the dataset constructed in this paper, which is approximately 40% higher than the cloud detection accuracy with large-scale samples. Additionally, it reduced the workload of masking a large number of samples for a specific region and realizing the possibility of efficient cloud detection in the region.
Ship classification in high-resolution (HR) synthetic aperture radar (SAR) images is an important and challenging task. The deep learning methods rely heavily on massive labeled data, which is not practical for SAR applications due to the lack of feature consistency under different sensors, angle of view, and scenarios, while the statistical methods are vulnerable to noise, image quality, and radiometric calibration and have poor robustness. To overcome these problems, a more robust superstructure scattering feature, named scattering bright line feature, is proposed to realize the convenient classification of ships in engineering applications. Based on this, a decision tree classifier for three types of merchant ships is developed. Four types of HR SAR slices are used to verify the validity of the new feature by both the decision tree and support vector machine classifiers, and the results show that the proposed feature has good classification performance in merchant ships with an overall accuracy higher than 80%.
KEYWORDS: Data modeling, 3D modeling, Data integration, Remote sensing, Visualization, Systems modeling, Databases, Pollution, Web services, 3D displays
Digital Earth is an integrated approach to build scientific infrastructure. The Digital Earth systems provide a
three-dimensional visualization and integration platform for river basin data which include the management data, in situ
observation data, remote sensing observation data and model output data. This paper studies the Digital Earth system
based river basin data integration technology. Firstly, the construction of the Digital Earth based three-dimensional river
basin data integration environment is discussed. Then the river basin management data integration technology is
presented which is realized by general database access interface, web service and ActiveX control. Thirdly, the in situ
data stored in database tables as records integration is realized with three-dimensional model of the corresponding
observation apparatus display in the Digital Earth system by a same ID code. In the next two parts, the remote sensing
data and the model output data integration technologies are discussed in detail. The application in the Digital Zhang
River basin System of China shows that the method can effectively improve the using efficiency and visualization effect
of the data.
KEYWORDS: Databases, Data centers, Standards development, Geographic information systems, Data storage, Web services, Information technology, Associative arrays, Internet, Information fusion
The Sustainable Development Information Sharing System (SDINFO) of China is a distributed information management
network system composed of databases of different types. This system consists of one general data center and 19
sub-centers. Housing the information provided by sectional administrative authorities, the system contains 1 TB volume
of data that can be shared in 224 fields of 37 categories under 5 major sectors. Of the volume, 30GB can be shared
through the central system. This paper introduces the content of those data, then the system architecture, and presents the
key technologies to manage the mass and varied data in the Sustainable Development Information Sharing System,
which are laid emphasis on. These technologies include multiple resource data standardization and reconstruction
technology, heterogeneous databases integration technology, mass spatial data manipulation technology, distributed
information publication technology, and information search technology, which are all discussed in details in this article.
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