With the improvement of remote sensing technology, the spatial, structural and texture information of land covers are
present clearly in high resolution imagery, which enhances the ability of crop mapping. Since the satellite RapidEye was
launched in 2009, high resolution multispectral imagery together with wide red edge band has been utilized in vegetation
monitoring. Broad red edge band related vegetation indices improved land use classification and vegetation studies.
RapidEye high resolution imagery was used in this study to evaluate the potential of red edge band in agricultural land
cover/use mapping using an objected-oriented classification approach. A new object-oriented decision tree classifier was
introduced in this study to map agricultural lands in the study area. Besides the five bands of RapidEye image, the
vegetation indexes derived from spectral bands and the structural and texture features are utilized as inputs for
agricultural land cover/use mapping in the study. The optimization of input features for classification by reducing
redundant information improves the mapping precision about 18% for AdaTree. WL decision tree, and 5% for SVM, the
accuracy is over 90% for both classifiers.
In recent years, many researches are about building 3D object model in the fields of computer vision and
photogrammetry, and camera calibration becomes the key problem. For long focal length digital camera calibration,
there's its particularity. In this paper, aiming at long focal length lens, a camera calibration method based on point-line
combination with vanishing points is proposed. This approach overcame the demerit of the conventional calibration
theory with vanishing points; therefore the precision of calibration parameters became better.
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.