Data Fusion of Scanned Black and White Aerial Photographs with Multispectral Satellite Images (Version 3, Approved)
|Reviewer 1 Apostolos Papakonstantinou Senior Post Doc Researcher, Marine Remote Sensing Group,Department of Marine Sciences, University of the Aegean, Mytilene, 81100, Greece||Reviewer 2 Dorian Gorgan UTCN, Romania|
Approved with revisions
Approved with revisions
Kaimaris, D.; Patias, P.; Mallinis, G.; Georgiadis, C. Data Fusion of Scanned Black and White Aerial Photographs with Multispectral Satellite Images. Sci 2020, 2, 29.
Kaimaris D, Patias P, Mallinis G, Georgiadis C. Data Fusion of Scanned Black and White Aerial Photographs with Multispectral Satellite Images. Sci. 2020; 2(2):29.Chicago/Turabian Style
Kaimaris, Dimitris; Patias, Petros; Mallinis, Giorgos; Georgiadis, Charalampos. 2020. "Data Fusion of Scanned Black and White Aerial Photographs with Multispectral Satellite Images." Sci 2, no. 2: 29.
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Senior Post Doc Researcher, Marine Remote Sensing Group,Department of Marine Sciences, University of the Aegean, Mytilene, 81100, Greece
In this manuscript, the authors are presenting the fusion of b/w aerial photography with multispectral satellite (Landsat-5) images having a few days difference in the acquisition date. More specific the authors are studying the addition of multispectral information from satellite images to black and white aerial photographs for classifying build and non-build areas in two Greek cities (Sparta and Pyrgos).
The manuscript is well organized, with the introduction having adequate references and the material and methods section describing in a correct manner the methods used. Moreover, in the results and discussion section, the authors are transparently presenting their findings.
Overall the manuscript is well written, and authors were presenting in detail all the necessary information for their approach; thus, I believe that should be published in the journal.
The following comments focus on the improvements that would benefit the manuscript.
In the abstract, the authors should change their statement
" from satellite images to black and white aerial photographs of the 2nd half of the 20th century (1950–1999)" to " from satellite images to black and white aerial photographs of the 80s decade (1980–1990)" as they only used data acquired in this time frame. The statement "2nd half of the 20th century" may confuse the readers as of the numbers and the time frame of the used datasets.
In table 2, authors should consider adding measurement units (meters) to their results.
In Figures 6 and 7 caption " rectangular rectangle." = " rectangle"
In Table 3 for Landsat data fusion results in Green column and Green Line "emph0.770" = "0.770".
In the 3.4 Classification subsection, the authors' stating that they used 35 classification classes that subsequently are grouped into two classes. Which are these classes, and how they are grouped? These 35 classes are, for example, the classes that used are part of the 44 Corine Classes?
Can this methodology be applied to currently available data (new high-resolution aerial data and Sentinel imagery)?
If the answer is positive, this should be stated in the manuscript as can be a crucial conclusion for applying this data fusion methodology, for example, to the automatic extraction of temporal information in Corine classification, reducing the photointerpretation process duration.
Response to Reviewer 1Sent on 13 Jul 2020 by Dimitris Kaimaris, Petros Patias, George Mallinis, Charalampos Georgiadis
This paper proves experimentally the fusion of black and white aerial photographs with multispectral satellite images (i.e. Landsat-5).
I do not understand or identified what are the main differences, regarding the experiments, between the two use cases of Sparta and Pyrgos. Why was it necessary to present both? Please argument.
Unfortunately Introduction includes some related works as well. Would be useful to add in introduction the structure of the paper, how do you organise the paper. It is not clear what are the objectives of the paper and how do you reach the goal through a logical and systematical approach.
It is quite difficult to identify and understand the steps of the experimental approach, from the beginning of the paper. They are presented just they are, without any previous argumentation. Maybe at the beginning of section 3, the steps that follow in the paper could be enumerated or briefly described.
Give more explanation on computing and the meaning of the correlation given in Tables 3 and 4. How do you assess the processing by analysing the correlation?
It would be useful to further detail and explain the Section "3.4. Area classifications and measurements ”. How do you decide on the dimension of the geographical areas? Why do you select just 35 classes? What is the meaning of a class? Is it dependent on the visual characteristics only or is it important as well their meaning? All these operations are performed manually/assisted? Why the computation of areas is a good enough metrics? Is it a metrics or is it just one of the objectives/assessment of the experiments?
It would be useful to present an algorithmic method based on a metrics for deciding which photographs and satellite images could be combined. How the decision and the processing could be assisted or automatically achieved?
Some editing mistakes:
- in Fig 3, would be more useful to exemplify by the map including location of Sparta and Pyrgos.
- Change separator "/" by ";" in the text "e.g., Ikonos-2 at nadir 1 m PAN, 4 m MS/QuickBird-2 at nadir 0.65 m PAN, 2.6 m MS/WorldView-4 at nadir 0.31 m PAN, 1.24 m MS" -> "e.g., Ikonos-2 at nadir 1 m PAN, 4 m MS; QuickBird-2 at nadir 0.65 m PAN, 2.6 m MS; WorldView-4 at nadir 0.31 m PAN, 1.24 m MS"
- "not nessecarly at the same" -> "not necessarily at the same".
- make the correction on "emph0.770" in Table 3.
- make correction "rectangular rectangle" in Fig 6 and Fig 7.