Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there...
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ISBN:
(纸本)9781402087349
Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up-to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat-TM/ETM+, IRS-ICID LISS-iii/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (similar to 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 in), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end-members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-iii classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications. Index Terms-Remote sensing, digital
Due to the high seismicity and high annual rainfall, numerous landslides occurred and caused severe impacts in Taiwan. Typhoon Morakot in 2009 brought extreme and long-time rainfall, and caused severe disasters. After...
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Due to the high seismicity and high annual rainfall, numerous landslides occurred and caused severe impacts in Taiwan. Typhoon Morakot in 2009 brought extreme and long-time rainfall, and caused severe disasters. After 2009, numerous large scale deep-seated landslides may still creeping, however not necessary easily to inspect the activity. In recent years, the remote sensing technology improves rapidly, providing a wide range of image, essential and precious geoinformation. Accordingly, the Small unmanned aircraft system (sUAS) has been widely used in landslide monitoring and geomorphic change detection. This study used UAS to continuously monitor a landslide area in Baolai Village in southern Taiwan, which had a catastrophic landslide event triggered by heavy rainfall caused by Typhoon Morakot in 2009. In order to accesses the potential hazards, this study integrates UAS, field geomatic survey, terrestrial laser scanner (ground LiDAR), and UAS LiDAR for sequential data acquisition since 2015. Based on the methods we are able to construct multi-temporal and high resolution DTMs, so as to access the activity and to monitoring the creeping landslides. The data set are qualified from 21 ground control points (GCPs) and 11 check points (CPs) based on real-time kinematic-global positioning system (RTK- GPS) and VBS RTK-GPS (e-GNSS). More than 10 UAS flight missions for the study areas dated since 2015, for an area large than 5-40 Km(2) with 8-12 cm spatial resolution (GSD). Then, the datasets was compared with the airborne LiDAR data, to evaluate the quality and the interpretability of the dataset. Since early 2018, we integrate UAS LiDAR technology to scanning the sliding area. The density of the point cloud data sets are higher than 250 and 100 points/m(2) for the total and ground point, respectively. The spatial distributions of geomorphologic changes were quantified firstly with the GCPS and CPs. The potential disaster was evaluated at different times, and the result
Given a coarse satellite image and a fine satellite image of a particular location taken at the same time, the high-resolution spatiotemporalimage fusion technique involves understanding the spatial correlation betwe...
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ISBN:
(纸本)9783031085307;9783031085291
Given a coarse satellite image and a fine satellite image of a particular location taken at the same time, the high-resolution spatiotemporalimage fusion technique involves understanding the spatial correlation between the pixels of both images and using it to generate a finer image for a given coarse (or test) image taken at a later time. This technique is extensively used for monitoring agricultural land cover, forest cover, etc. The two key issues in this technique are: (i) handling missing pixel data and (ii) improving the prediction accuracy of the fine image generated from the given test coarse image. This paper tackles these two issues by proposing an efficient method consisting of the following three basic steps: (i) imputation of missing pixels using neighborhood information, (ii) cross-scale matching to adjust both the Point Spread Functions Effect (PSF) and geo-registration errors between the course and high-resolution images, and (iii) error-based modulation, which uses pixel-based multiplicative factors and residuals to fix the error caused due to modulation of temporal changes. The experimental results on the real-world satellite imagery datasets demonstrate that the proposed model outperforms the state-of-art by accurately producing the high-resolution satellite images closer to the ground truth.
The US Military is increasingly relying on the use of unmanned aerial vehicles (UAV) for intelligence, surveillance, and reconnaissance (ISR) missions. Complex arrays of Full-Motion Video (FMV), Wide-Area Motion Imagi...
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ISBN:
(纸本)9780819490384
The US Military is increasingly relying on the use of unmanned aerial vehicles (UAV) for intelligence, surveillance, and reconnaissance (ISR) missions. Complex arrays of Full-Motion Video (FMV), Wide-Area Motion Imaging (WAMI) and Wide Area Airborne Surveillance (WAAS) technologies are being deployed on UAV platforms for ISR applications. Nevertheless, these systems are only as effective as the image Analyst's (IA) ability to extract relevant information from the data. A variety of tools assist in the analysis of imagery captured with UAV sensors. However, until now, none has been developed to extract and visualize parallax three-dimensional information.(1) Parallax Visualization (PV) is a technique that produces a near-three-dimensional visual response to standard UAV imagery. The overlapping nature of UAV imagery lends itself to parallax visualization. Parallax differences can be obtained by selecting frames that differ in time and, therefore, points of view of the area of interest. PV is accomplished using software tools to critically align a common point in two views while alternately displaying both views in a square-wave manner. Humans produce an autostereoscopic response to critically aligned parallax information presented alternately on a standard unaided display at frequencies between 3 and 6 Hz.(2) This simple technique allows for the exploitation of spatial and temporal differences in image sequences to enhance depth, size, and spatial relationships of objects in areas of interest. PV of UAV imagery has been successfully performed in several US Military exercises over the last two years.(3)
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