摘要 :
On March 11, 2011, the magnitude 9 Tohoku earthquake and resulting tsunami struck off the coast of Japan. An estimated over 400,000 persons were displaced from their homes and the damage to the coastline and nearby urban areas was...
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On March 11, 2011, the magnitude 9 Tohoku earthquake and resulting tsunami struck off the coast of Japan. An estimated over 400,000 persons were displaced from their homes and the damage to the coastline and nearby urban areas was extensive. Additionally, the combined effects of the earthquake and tsunami caused damage to the Fukushima Dai'ichi Nuclear Power Station. As part of the International Charter "Space and Major Disasters", the US Geological Survey coordinated a volunteer effort to aid in the response to the disaster. The goal of the project was to produce maps derived from civilian (NASA Landsat and ASTER) and commercially available (DigitalGlobe and GeoEye), high resolution satellite imagery to be delivered to the Japanese authorities. RIT, as part of our Information Products Laboratory for Emergency Response (IPLER) program, was one of the organizations involved in this effort. This paper describes the timeline of the response, the challenges faced in this effort, the workflow developed, and the products that were distributed. Lessons learned from the response will also be described to aid the remote sensing community in preparing for responses to future natural disasters.
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摘要 :
On March 11, 2011, the magnitude 9 Tohoku earthquake and resulting tsunami struck off the coast of Japan. An estimated over 400,000 persons were displaced from their homes and the damage to the coastline and nearby urban areas was...
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On March 11, 2011, the magnitude 9 Tohoku earthquake and resulting tsunami struck off the coast of Japan. An estimated over 400,000 persons were displaced from their homes and the damage to the coastline and nearby urban areas was extensive. Additionally, the combined effects of the earthquake and tsunami caused damage to the Fukushima Dai'ichi Nuclear Power Station. As part of the International Charter "Space and Major Disasters", the US Geological Survey coordinated a volunteer effort to aid in the response to the disaster. The goal of the project was to produce maps derived from civilian (NASA Landsat and ASTER) and commercially available (DigitalGlobe and GeoEye), high resolution satellite imagery to be delivered to the Japanese authorities. RIT, as part of our Information Products Laboratory for Emergency Response (IPLER) program, was one of the organizations involved in this effort. This paper describes the timeline of the response, the challenges faced in this effort, the workflow developed, and the products that were distributed. Lessons learned from the response will also be described to aid the remote sensing community in preparing for responses to future natural disasters.
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Increasing the spatial resolution of panchromatic images and multispectral images is a classical problem in remote sensing. However, it is still in its infancy to spatially enhance the resolution of hyperspectral imageries. In thi...
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Increasing the spatial resolution of panchromatic images and multispectral images is a classical problem in remote sensing. However, it is still in its infancy to spatially enhance the resolution of hyperspectral imageries. In this paper, we proposed a new method for increasing the spatial resolution of a hyperspectral data cube by using an iterative back projection (IBP) based method. Also, we developed a new metric to measure the visual quality of the enhanced images. This metric is good at measuring the visual quality of an image whose full-reference image is not available whereas the low spatial resolution image is available. Experimental results confirm the superiority of the proposed method.
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摘要 :
Increasing the spatial resolution of panchromatic images and multispectral images is a classical problem in remote sensing. However, it is still in its infancy to spatially enhance the resolution of hyperspectral imageries. In thi...
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Increasing the spatial resolution of panchromatic images and multispectral images is a classical problem in remote sensing. However, it is still in its infancy to spatially enhance the resolution of hyperspectral imageries. In this paper, we proposed a new method for increasing the spatial resolution of a hyperspectral data cube by using an iterative back projection (IBP) based method. Also, we developed a new metric to measure the visual quality of the enhanced images. This metric is good at measuring the visual quality of an image whose full-reference image is not available whereas the low spatial resolution image is available. Experimental results confirm the superiority of the proposed method.
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摘要 :
Increasing the spatial resolution of panchromatic images and multispectral images is a classical problem in remote sensing. However, it is still in its infancy to spatially enhance the resolution of hyperspectral imageries. In thi...
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Increasing the spatial resolution of panchromatic images and multispectral images is a classical problem in remote sensing. However, it is still in its infancy to spatially enhance the resolution of hyperspectral imageries. In this paper, we proposed a new method for increasing the spatial resolution of a hyperspectral data cube by using an iterative back projection (IBP) based method. Also, we developed a new metric to measure the visual quality of the enhanced images. This metric is good at measuring the visual quality of an image whose full-reference image is not available whereas the low spatial resolution image is available. Experimental results confirm the superiority of the proposed method.
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The Wildfire Airborne Sensor Program (WASP) is an imaging system designed, built, and operated by the RIT Center for Imaging Science. The system consists of four cameras: a high resolution color camera and SWIR, MWIR, and LWIR cam...
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The Wildfire Airborne Sensor Program (WASP) is an imaging system designed, built, and operated by the RIT Center for Imaging Science. The system consists of four cameras: a high resolution color camera and SWIR, MWIR, and LWIR cameras. When flown with our corporate partners, Kucera International, the imaging system is combined with a high-resolution LIDAR. This combination provides a full-spectrum, multimodal data collection platform unique to RIT. Under funding by the World Bank, the WASP system was used to image over 250 sq. mi. in Haiti (approximately 15,000 visible and 45,000 infrared frames) from January 21 - 27, 2010 in support of the earthquake relief efforts. Priorities of collection were the area surrounding Port au Prince, the city of Leogane, several other badly damaged towns, and, at the request of the USGS, a high resolution LIDAR collection over the fault line. The imagery was used in the field by disaster relief workers and by collaborators at the University of Buffalo and ImageCat, Inc. to perform building damage and road network trafficability assessments. Additionally, large area mosaics and semi-automatic processing algorithms were developed for value-added product development. In particular, a methodology was developed to extract the locations of blue tarps (indicative of displaced persons) from the images. All imagery was made available to the public through outlets such as Google Earth, the University of Buffalo, the US Geological Survey, the United Nations, and other sites.
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The Wildfire Airborne Sensor Program (WASP) is an imaging system designed, built, and operated by the RIT Center for Imaging Science. The system consists of four cameras: a high resolution color camera and SWIR, MWIR, and LWIR cam...
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The Wildfire Airborne Sensor Program (WASP) is an imaging system designed, built, and operated by the RIT Center for Imaging Science. The system consists of four cameras: a high resolution color camera and SWIR, MWIR, and LWIR cameras. When flown with our corporate partners, Kucera International, the imaging system is combined with a high-resolution LIDAR. This combination provides a full-spectrum, multimodal data collection platform unique to RIT. Under funding by the World Bank, the WASP system was used to image over 250 sq. mi. in Haiti (approximately 15,000 visible and 45,000 infrared frames) from January 21 - 27, 2010 in support of the earthquake relief efforts. Priorities of collection were the area surrounding Port au Prince, the city of Leogane, several other badly damaged towns, and, at the request of the USGS, a high resolution LIDAR collection over the fault line. The imagery was used in the field by disaster relief workers and by collaborators at the University of Buffalo and ImageCat, Inc. to perform building damage and road network trafficability assessments. Additionally, large area mosaics and semi-automatic processing algorithms were developed for value-added product development. In particular, a methodology was developed to extract the locations of blue tarps (indicative of displaced persons) from the images. All imagery was made available to the public through outlets such as Google Earth, the University of Buffalo, the US Geological Survey, the United Nations, and other sites.
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This work presents various normalized difference water indices (NDWI) to delineate lakes from Schumacher Oasis, East Antarctica, by using a very high resolution WorldView-2 (WV-2) satellite imagery. Schirmacher oasis region hosts ...
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This work presents various normalized difference water indices (NDWI) to delineate lakes from Schumacher Oasis, East Antarctica, by using a very high resolution WorldView-2 (WV-2) satellite imagery. Schirmacher oasis region hosts a number of fresh as well as saline water lakes, such as epishelf lakes, ice-free or landlocked lakes, which are completely frozen or semi-frozen and in a ice-free state. Hence, detecting all these types of lakes distinctly on satellite imagery was the major challenge, as the spectral characteristics of various types of lakes were identical to the other land cover targets. Multiband spectral index pixel-based approach is most experimented and recently growing technique because of its unbeatable advantages such as its simplicity and comparatively lesser amount of processing-time. In present study, semiautomatic extraction of lakes in cryospheric region was carried out by designing specific spectral indices. The study utilized number of existing spectral indices to extract lakes but none could deliver satisfactory results and hence we modified NDWI. The potentials of newly added bands in WV-2 satellite imagery was explored by developing spectral indices comprising of Yellow (585 - 625 nm) band, in combination with Blue (450 - 510 nm), Coastal (400 - 450 nm) and Green (510 - 580 nm) bands. For extraction of frozen lakes, use of Yellow (585 - 625 nm) and near-infrared 2 (NIR2) band pair, and Yellow and Green band pair worked well, whereas for ice-free lakes extraction, a combination of Blue and Coastal band yielded appreciable results, when compared with manually digitized data. The results suggest that the modified NDWI approach rendered bias error varying from ～1 to ～34 m~2.
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This work presents various normalized difference water indices (NDWI) to delineate lakes from Schumacher Oasis, East Antarctica, by using a very high resolution WorldView-2 (WV-2) satellite imagery. Schirmacher oasis region hosts ...
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This work presents various normalized difference water indices (NDWI) to delineate lakes from Schumacher Oasis, East Antarctica, by using a very high resolution WorldView-2 (WV-2) satellite imagery. Schirmacher oasis region hosts a number of fresh as well as saline water lakes, such as epishelf lakes, ice-free or landlocked lakes, which are completely frozen or semi-frozen and in a ice-free state. Hence, detecting all these types of lakes distinctly on satellite imagery was the major challenge, as the spectral characteristics of various types of lakes were identical to the other land cover targets. Multiband spectral index pixel-based approach is most experimented and recently growing technique because of its unbeatable advantages such as its simplicity and comparatively lesser amount of processing-time. In present study, semiautomatic extraction of lakes in cryospheric region was carried out by designing specific spectral indices. The study utilized number of existing spectral indices to extract lakes but none could deliver satisfactory results and hence we modified NDWI. The potentials of newly added bands in WV-2 satellite imagery was explored by developing spectral indices comprising of Yellow (585 - 625 nm) band, in combination with Blue (450 - 510 nm), Coastal (400 - 450 nm) and Green (510 - 580 nm) bands. For extraction of frozen lakes, use of Yellow (585 - 625 nm) and near-infrared 2 (NIR2) band pair, and Yellow and Green band pair worked well, whereas for ice-free lakes extraction, a combination of Blue and Coastal band yielded appreciable results, when compared with manually digitized data. The results suggest that the modified NDWI approach rendered bias error varying from ～1 to ～34 m~2.
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Estimating the aboveground biomass of forests still remains a difficult task. Empirical models based on image
reflectance or radar backscattering saturate above a certain biomass level. It was shown that the 3D data generated
by...
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Estimating the aboveground biomass of forests still remains a difficult task. Empirical models based on image
reflectance or radar backscattering saturate above a certain biomass level. It was shown that the 3D data generated
by scanning lidars (Light Detection and Ranging) overcomes the saturation problem and yield accurate results.
Regression of metrics derived from the lidar-measured canopy height distribution against field estimates of biomass
gives promising results. However, most lidar-based biomass prediction studies so far were performed on
monospecific stands. Because of the differences in crown shape between species, the lidar metrics are influenced by
species composition. We hypothesize that biomass prediction equations must be recalibrated for different species. In
this fine scale study, we propose to derive species information from a QuickBird image.
Lidar data and a concomitant QuickBird image set were acquired over a 200 km2 region of the southern boreal
forest of western Quebec. The specific diameter at breast height of trees was measured in the field for 40 plots.
Regionally calibrated specific allometric equations were used to estimate in situ biomass. The plots were classified
into pure deciduous, pure conifers, and mixed based on the field data. Canopy height derived from the lidar data was
integrated with the QuickBird spectral signatures to automatically classify the tree species into deciduous and
conifer. The plots were then classified into the deciduous, conifer and mixed classes based on the results of the
multispectral classification. These in situ estimates of biomass were regressed against lidar metrics derived from the
canopy height model. Different prediction models were developed: a general model for all species, specific models,
and a mixed model (combination of specific model). The use of specific models largely increased the prediction
accuracy for the conifer and mixed class. Predicting the biomass of deciduous trees was however relatively
innaccurate. When the models were chosen based on the image classification, the overall biomass prediction error
was only slightly better than that of the general model because of classification error.
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