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In this paper a new orientation model in the form of self-calibrating DLT SDLT is derived for the pushbroom image orientation. The new model does not require any sensor parameters like the interior orientation the incident angle and the ephemeris information also does not require any geometric pre-correction of the original image data. It is very convenient to be used with different kinds of linear sensor images especially advantageous for proces...
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The techniques are also used to analyze image data, for their own reasons. With the help of several techniques from different approaches of automatic image matching and data identification, the automatic tie point collection is also described and applied. CATEGORY: Computer Vision RESPONSE TO THE REVIEWER: I have reviewed the paper and found your critique helpful. I would like to have more details about automatic line detector detection, but I think you are a bit limited in your understanding about the different approaches. Your description is too brief or non-clear. The use of the “LDA” method to estimate the position of each image in a block is an approach that has been used in automated image matching. I would recommend some mentions of the various approaches used for automatic identification, transfer and measurement of image tie points, and it must be noted that they have been applied in different ways and that the use of the LDA techniques is very different from the use of the LDA for the automatic tie point estimation. In your paper, you mention the use of some combination of several approaches for different purpose. I did not see your approach in your paper or elsewhere. In your paper, it states that “With the help of several different techniques from different approaches of automatic image matching and data identification, the automatic tie point collection is also described and applied.” This is a bit vague, but I just hope you are saying that the automatic tie point collection, is one of the approaches considered. The use of automated detection techniques does not necessarily lead to automated identification of the image tie points. The use of automatic tie points estimation is for application in automatic identification with respect to the image. The techniques are in several approaches. The “LDA” and the automatic “Tie Point Detection” and “Wang” approaches are two different approaches. It is a bit vague to explain in the paper. Your critique is a great help! I will be looking forward to reading your book, if such a thing is available at some point. Thank you for the feedback. We have been using automated detection techniques for a long time. P.s. I've been meaning to write a comment in this paper, but the blog format doesn't make it easy to format properly.

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Wang automated triangulation is a process of using computer algorithms to calculate the positions of points in a triangulation network.
Wang automated triangulation is typically performed by surveyors or geodetic engineers who are responsible for establishing and maintaining accurate spatial reference systems.
Filling out wang automated triangulation involves inputting the measured coordinates of control points into the software, running the triangulation algorithm, and reviewing the calculated positions for accuracy.
The purpose of wang automated triangulation is to accurately determine the positions of points in a triangulation network, which can be used for various applications such as mapping, surveying, and geospatial analysis.
The information that must be reported on wang automated triangulation includes the coordinates of control points, the triangulation algorithm used, any adjustments made to the calculated positions, and a description of the accuracy assessment.
The deadline to file wang automated triangulation in 2023 may vary depending on the specific requirements of the project or organization. It is recommended to check the relevant guidelines or regulations to determine the exact deadline.
The penalty for late filing of wang automated triangulation depends on the specific regulations and policies of the project or organization. It could include monetary fines, loss of credibility, or other consequences as specified in the guidelines or contracts.
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