Based on a few ground control points, the affine transform model, a polynomial model and rational function model are adopted to correct the ZY-3 remote sensing imagery. Because of the traffic and economic issues only 7 ground control points (GCPs) are collected with high location accuracy, and they all only employed to solve the coefficient of the geometrical transformation model. So there are no residual GCP for the evaluation of the correction accuracy and an intuitive and effective accuracy evaluation method is presented for correction result superimposed with topographic maps to verify the geometric correction accuracy. The experimental result shows that having difficulty in obtaining sufficient quantity of the control points, rational function model could be recommended to correct the ZY-3 imagery and obtain the corrective result with relatively high accuracy, which has certain application value, and it is a good method in absence of GCP to use topographic map to evaluate the correction accuracy.
Proc. SPIE. 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
KEYWORDS: Digital image processing, Digital photography, Image processing, Photography, 3D modeling, Geographic information systems, Data acquisition, Photogrammetry, Information technology, 3D image processing
Photogrammetry is the study of information by using image acquisition, processing, extraction and results in expression of a craft, science and technology. The photogrammetry can be used to test the various scales of topographic maps. Photogrammetry has become a discipline intersection of righteousness and computer technology. The expensive equipment that photography measurement requires has gradually been replaced by computer and its related input and output devices. Particularly , the integration of photogrammetry technology with GPS, LIDAR and GIS, photogrammetry has become a professional required course. Practice is a bottleneck that restricts the teaching effect of Photogrammetry. Its content is a basis of new technology such as UAV mapping and oblique photogrammetry. In 2007 the Ministry of Education officially launched the undergraduate teaching quality and teaching reform project. The colleges and universities in China have actively carried out the reform and practice of applied and innovative talents training and explore the mode and way of cultivating new talents. To effectively improve the effect of practice and innovation ability of student, Heilongjiang institute of technology has constructed the experimental platform of simulation and emulation of photogrammetry, so that the students can complete the practice in simulation environment, explore the new mode of photogrammetric practice, and improve the quality of personnel training.
Considering the problem in monitoring agricultural condition in the semi-arid areas of Northwest of China, we propose a new method for estimation of crop planting area, using the single phase optical and microwave remote sensing data collaboratively, which have demonstrated their respective advantages in the extraction of surface features. In the model, the ASAR backscatter coefficient is normalized by the incident angle at first, then the classifier based on Bayesian network is developed, and the VV, VH polarization of ASAR and all the 7 TM bands are taken as the input of the classifier to get the class labels of each pixel of the images. Moreover the crop planting areas can be extracted by the classification results. At last, the model is validated for the necessities of normalization by the incident angle and integration of TM and ASAR respectively. It results that the estimation accuracy of crop planting area of corn and other crops garden are 98.47% and 78.25% respectively using the proposed method, with an improvement of estimation accuracy of about 3.28% and 4.18% relative to single TM classification. These illustrate that synthesis of optical and microwave remote sensing data is efficient and potential in estimation crop planting area.
This paper analyses the forest landscape diversity of the study area with the help of ArcGIS10 and GS+ software. The forest landscape diversity and spatial interpolation and spatial differentiation are also carried out. The result shows that the maximum value of SHDI in 1997is 2.0463 and the minimum value is 0.2544 , which are 1.9722 and 0.2418 in the year of 2009. The advantage religion of SHDI mainly distributes in the middle of the study region , showing a band region from southwest to northeast . The forest landscape diversity and the space location have a moderate spatial correlation and a obvious structural under a forest level.