Large Aperture Static Interference Imaging Spectrometer is a new type of spectrometer with light structure, high spectral
linearity, high luminous flux and wide spectral range, etc ,which overcomes the contradiction between high flux and high
stability so that enables important values in science studies and applications. However, there’re different error laws in
imaging process of LASIS due to its different imaging style from traditional imaging spectrometers, correspondingly, its
data processing is complicated. In order to improve accuracy of spectrum detection and serve for quantitative analysis and
monitoring of topographical surface feature, the error law of LASIS imaging is supposed to be learned. In this paper, the
LASIS errors are classified as interferogram error, radiometric correction error and spectral inversion error, and each type
of error is analyzed and studied. Finally, a case study of Yaogan-14 is proposed, in which the interferogram error of LASIS
by time and space combined modulation is mainly experimented and analyzed, as well as the errors from process of
radiometric correction and spectral inversion.
Stereo adjustment is the key to stereo mapping of domestic optical satellite image. In recent years, the most common satellite data used for stereo mapping was the along-track stereo pairs from the tri-linear CCD mapping camera in domestic. With the improvement of satellite mobility and the measurement accuracy of orbit and attitude, using the single line array CCD camera, by side sway, to obtain across-track stereo pairs becomes possible. In this paper, the YG- 24 satellite panchromatic very high resolution (HR) images were used to obtain the across-track stereo pairs. Based on undistorted RFM, the stereo pairs were processed with affine transformation model under the condition of different number of ground control points (GCPs). The result shows that, the stereo positioning accuracy of bundle adjustment can reach 35m without GCPs; while the root mean square errors (RMSEs) of plane is 0.7m and the RMSEs of altitude is 0.9m with GCPs, which can meet the secondary accuracy requirements of 1:25000 mapping.
With frequent occurrence of earthquake disaster, earthquake monitoring becomes increasingly concerned. Global
observing by optical remote sensing is an emerging technology widely applied in monitoring temporal changes of
topography in earthquake. It provides advantages of large width of observation, fast data acquisition and high time
effectiveness. This technique takes advantages of accurate image registration of pre-seismic and post-seismic to spot
surface rupture zones. Therefore, the spatial alignment accuracy of multi temporal images becomes a problem that hinder
the earthquake monitoring. Considering the adverse impact of different imaging angle, camera lens distortion and other
factors on image registration, a new approach of high accurate registration based on constraining positioning consistency
in rational function model (RFM) is proposed. Ziyuan3 images of Yutian country in Xinjiang are used to perform the
earthquake monitoring experiment. After applying the proposed method, registration accuracy of pre-seismic and postseismic
images is better than 0.6 pixel; surface rupture zones caused by earthquake are acquired promptly.
The technology of compounding LIDAR with visible imaging topographic mapping in space is firstly proposed in this paper. We introduce the design project of integration for laser and visible system in detail, and create the geometrical model for LIDAR Compounded with Visible Imaging Topographic Mapping. Using simultaneous adjustment test with true satellite data, it is verified that the system of LIDAR compounded with visible Imaging topographic mapping meets the designed technology index.
Owing to relatively simplistic domestic hardware technology and a lack of on-orbit geometric calibration, particularly interior calibration, the positioning accuracies of Optical-1 HR satellites can vary greatly depending on the presence of ground control points (GCPs). Without GCPs, accuracies are typically lower than 100 pixels, whereas when GCPs are plentiful, accuracy is higher than one pixel, demonstrating the potential for a large discrepancy between international optical satellites with the same image resolution. This study investigated a new method of geometric calibration for Optical-1 HR satellites. Experiments were conducted to demonstrate the positive effects on positioning accuracy achieved by the calibration method. After calibration by our method, a positioning accuracy of higher than one pixel was obtained with only a small number of GCPs, which is equivalent to the accuracy of advanced international optical satellites with the same image resolution.
Forest cover maps are essential for current researches of biomass estimation and global change, but traditional methods
to derive forest maps are complex. These methods usually need training samples or other ancillary data as input, and are
time- and labor- consuming for large scale applications. To make the process of forest cover mapping simple and rapid,
in this paper, a simple spectral index called forest index (FI) was proposed to highlight forest land cover in Landsat
scenes. The FI is derived from three bands, green, red and near-infrared (NIR) bands and an FI image can be classified
into forest/non-forest map with a threshold. The overall accuracies of classification maps in the two study areas were
97.8% and 96.2%, respectively, which indicates that the FI is efficient at highlighting forest cover.
This study analyzes the change of Normalized Difference Vegetation Index (NDVI) and precipitation for forest in
different ecological zones in China and their correlation over the period of 1982-2006. The specific aim of this paper was
to identify the changing trends of NDVI and precipitation and understand their relations, especially, on which duration
the precipitation influence NDVI strongly during growing season of forest in different ecological aspects. The results
showed that 1) the break points of NDVI and precipitation appeared in different years in most ecological zones, but in
temperate continental forest and temperate mountain system, they have a high degree of consistency; 2) the NDVI in
boreal coniferous forest, temperate mountain system and tropical moist deciduous forest showed a increasing trend
during 1982-2006 and the lowest value were appeared in different time and the precipitation in boreal coniferous forest
and temperate mountain system showed a decreasing trend; 3) the forest in different ecological zones has different
patterns with different periods and lags and the peak value of pearson correlation coefficients were showed in different
duration and lag, and NDVI and precipitation generally have the negative but weak relation.
Image segmentation is the foundation of the object-based and automatic interpretation of remote sensing images , but the
high-resolution remote sensing image data is generally large, for this problem, the traditional approach is generally
processing in sub-block, and then merge the results, but because of the complexity of the nature object, the merging
result is not satisfied, and the segmentation algorithm is often more complex to calculate time-consuming, and it affect
the image automatic interpretation of real-time. In this paper, we propose a parallel segmentation algorithm based on
pyramid image, first of all, we create the pyramid image and segment it with the initial homogeneous regions were got, it
divide the data according to the initial homogeneous regions and segment them from the top of pyramid image to the
bottom with data parallelism, and it improve segmented efficiency, at the same time, it can avoid the problem of
“merging line” when merging of the segmenting results in different image block. Experimental results show that the
result of this algorithm is almost the same as the result of Mean Shift algorithm segmentation case; it says that this
algorithm is correct and reliability, it also shows that this algorithm is efficiency by comparing the use of time between
serial segmentation and parallel segmentation.
In satellite mapping application area, geometric quality assessment for remote sensing image compression is of great importance for onboard compression index determination. The paper proposed an integral geometric quality assessment plan for remote sensing image compression, which includes image matching accuracy assessment, effects of compression on automated DSM/DEM extraction, and photogrammetic point determination accuracy assessment. Image
matching accuracy analysis shows how degradation in image quality associated with lossy compression can affect matching accuracy. In analyzing effects of compression on automated DSM/DEM extraction, a DSM is extracted from the original stereopair and held as the reference against which the terrain heights obtained from compressed imagery are compared. Similar to DSM extraction accuracy analysis, photogrammetric point determination accuracy analysis is
proposed to compare the accuracy of two sets of 3D coordinates of the feature points which are from original images and reconstructed images. The relationship between compression ratio and terrain types was examined. As to SPIHT algorithm adopted in Resources Satellite-3, the experiment results showed that the compression ratio should be no more than 4:1 for mapping application.
Quality assessment for remote sensing image compression is of great significance in many practical applications. A
comprehensive index based on muti-dimensional structure model was designed for image compression assessment,
which consists of gray character distortion dimension, texture distortion dimension, loss of correlation dimension. Based
on this model, a new comprehensive image quality index-Q was proposed. In order to assess the agreement between our
comprehensive image quality index Q and human visual perception, we conducted subjective experiments in which
observers ranked reconstructed images according to perceived distortion. For comparison, PSNR is introduced. The
experiments showed that Q had a better consistency with subjective assessment results than conventional PSNR.
This paper proposes a new approach to image matching by epipolar constraint and local reliability constraint for Remote Sensing Image. We define a new measure of matching support according to the local reliability constraint. A new search strategy -- Self-Diagnosis is developed for robust image matching. This strategy only selects those matches having both high matching support and low matching ambiguity. The proposed algorithm has been tested and works remarkably well in remote sensing imagery stereo pairs.
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