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A dust storm detection algorithm for NPP VIIRS data is proposed in this paper. The pixel dataset includes a variety of typical feature types, such as dust over different surface type, thick and thin clouds, vegetation, Gobi, ice/snow, etc. were collected and the distribution of the reflectance and brightness temperature were analyzed, based on which, a dust detection algorithm was generated. Multi-temporal...
Airborne light detection and ranging (LiDAR) technology is an active remote sensing technique that produces a true orthophoto at a single wavelength. LiDAR is not as dependent on the weather as optical sensors, and LiDAR features have been widely applied for characterizing land cover classes of interest. Instead of using point-based features for classification, object-based LiDAR features were employed...
UDTCDA (universal dynamic threshold cloud detection algorithm) is a new cloud detection method which was proposed recently. This cloud detection method is supported by priori surface reflectance obtained from MODIS surface reflectance product (MOD09). Reflectance of four bands in the wavelength of visible to near infrared are used to detect the cloudy pixels. Because there is a priori reference data...
Water extraction in arid regions such as north Xinjiang is particularly important due to their lack of water. During the last decades, water resources in north Xinjiang have been seriously impacted by more and more coal-mining activities. Accurate extraction of rivers and broad waters in this region is becoming requisite. However, there are some limitations for existing methods to extract small rivers...
Trajectories obtained from Global Position System (GPS)-enabled taxis grant us an opportunity not only to extract meaningful statistics, dynamics, and behaviors about certain urban road users but also to monitor adverse and/or malicious events. In this paper, we focus on the problem of detecting anomalous routes by comparing the latter against time-dependent historically “normal” routes. We propose...
Normalized Difference Vegetation Index (NDVI) has been widely used in evaluating the status and changes of ecological environment. This paper aims to introduce a method of extracting NDVI from image gained by HJ-1A/B CCD boarding on HJ-1A/B satellite. Cloud detection and atmospheric correction are carried out before the NDVI calculated. Results show that the NDVI can well describe the vegetation distribution.
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