Pixel-level image fusion

Coupled nonnegative matrix factorization (CNMF) unmixing is proposed for the fusion of low-spatial-resolution hyperspectral and high-spatial-resolution multispectral data to produce fused data with high spatial and spectral resolutions. Both hyperspectral and multispectral data are alternately unmixed into end member and abundance matrices by the CNMF algorithm based on a linear spectral mixture model. Sensor observation models that relate the two data are built into the initialization matrix of each NMF unmixing procedure. This algorithm is physically straightforward and easy to implement owing to its simple update rules. Simulations with various image data sets demonstrate that the CNMF algorithm can produce high-quality fused data both in terms of spatial and spectral domains, which contributes to the accurate identification and classification of materials observed at a high spatial resolution.

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Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion

Multi- and hyperspectral geologic remote sensing: A review

Geologists have used remote sensing data since the advent of the technology for regional mapping, structural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio techniques and selective principal component analysis to produce iron oxide and hydroxyl images that could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the thermal region allowed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc.). The step toward quantitative and validated (subpixel) surface mineralogic mapping was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectra and to unravel mixed pixel spectra to pure endmember spectra to derive subpixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) data continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochemistry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly workflows should be multidisciplinary and remote sensing data should be integrated with field observations and subsurface geophysical data to monitor and understand geologic processes.

Multi - and hyperspectral geologic remote sensing : a review

Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information. They have made a plethora of applications feasible for the analysis of large areas of the Earth?s surface. However, a significant number of factors-such as the high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light-scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions-make such data inherently nonlinear and complex, which poses major challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide.

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Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Hyperspectral Imager Suite (HISUI) is a next-generation Japanese optical sensor that is composed of a hyperspectral imager and a multispectral imager, which will be launched on Advanced Land Observation Satellite 3 (ALOS-3). Combination of hyperspectral and multispectral imagers contributes land remote sensing with high spectral and spatial resolution. Calibration of the HISUI functionalequivalent model has been carried out to meet the science requirements on the sensor. A center wavelength and spectral response of each band of the hyperspectral sensor is obtained, from which distortions in a spectrometer in terms of smile and keystone properties are investigated. The results of the calibration activity for the functionalequivalent model show appropriate performance of the sensors.

Hyperspectral Imager Suite (HISUI) -Japanese hyper-multi spectral radiometer

The hyperspectral and the multispectral (hyper-multi spectral) mission is the Japanese next generation space-borne radiometer development project. This project is a heritage from ASTER launched in December 1999. The performance of the hyperspectral radiometer is 30m ground sampling distance, 30km swath width, 10nm and 12.5nm wavelength distance for VNIR and SWIR respectively, over 450@620nm and 300@2,100nm of the signal to noise (S/N) ratio. The performance of the multispectral radiometer is 5m ground sampling distance, 90km swath width, over 200 for all bands of S/N ratio. This project will be launched on ALOS-3 of JAXA in FY2014. The panchromatic sensor with stereo viewing will also be installed on ALOS-3.

http://geodesy.unr.edu/hanspeterplag/library/IGARSS2010/pdfs/3956.pdf

Japanese hyper-multi spectral mission

Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS’s performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost.

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Onboard Image Processing System for Hyperspectral Sensor.

Hyperspectral Imager Suite (HISUI) is a next-generation Japanese optical sensor that is composed of a hyperspectral imager and a multispectral imager. Combination of hyperspectral and multispectral imagers contributes land remote sensing with high spectral and spatial resolution. Validation of the HISUI evaluation model, including environmental test such as vibration test and thermal-vacuum test has been carried out to meet the science requirements on the sensor under the space-borne conditions. Major performances of the sensors are obtained. Spectral response of each band of the hyperspectral sensor, distortions in a spectrometer in terms of smile and keystone properties, are investigated. The test results of the evaluation model show appropriate performance of the sensors.

Results of evaluation model of Hyperspectral Imager Suite (HISUI)

The hyper-multi spectral mission named HISUI (Hyper-spectral Imager SUIte) is the next Japanese earth observation
project that will be on board ALOS-3 satellite. This project is the follow up mission of the Advanced Spaceborne
Thermal Emission and reflection Radiometer (ASTER). HISUI is composed of hyperspectral radiometer with higher
spectral resolution and multi-spectral radiometer with higher spatial resolution. The functional evaluation model is under
development to confirm the spectral and radiometric performance prior to the flight model manufacture phase. This
model contains the VNIR and SWIR spectrograph, the VNIR and SWIR detector assemblies with a mechanical cooler
for SWIR, signal processing circuit and on-board calibration source.

Hitomi Inada

Papers

Hyperspectral Imager Suite (HISUI) -Japanese hyper-multi spectral radiometer

Japanese hyper-multi spectral mission

Onboard Image Processing System for Hyperspectral Sensor.

Results of evaluation model of Hyperspectral Imager Suite (HISUI)

The functional evaluation model for the on-board hyperspectral radiometer