In my first GIS-course I was presented with the concept of quality measures for a georeferenciation. My lecturer taught me: “There it is: the RMSE or root mean square error. Keep in mind: it should be not bigger than half the pixel size!” That was all I got.
If you facing comparable words stick with us!
The RMSE is a distance measuring concept to compare expected with measured data. [Q] Where is the connection with georeferencing an image? [A] If you are doing a georeferenciation you are using a model to perform the transition from pixel-positions in your image to geographical coordinates. This model is calculating expected data.
As an example we will stick with an affine projection of an image.
An affine projection includes translation, compaction / expansion, rotation and combinations of these. To keep it more simple, we will concentrate now only on pixels in direction of x (longitudes):
Of course the linear model or the connection between pixel numbers and longitudes is “already” defined by two points (if want to transfer this to x and y values: a affine transformation (polynomial transformation of 1st degree) of an image is defined by three points). But we are all producing errors: If you are using GCPs (Ground Control Points, like in an aerial photograph) or map coordinates: the data you are using is full of possible errors. To reduce your errors it is better to search and use more points and define their real-world coordinates. But doing so will result in another model. So you are changing your model which will try to fit to your data similar to a regression analysis:
The best model will reduce the differences between your defined coordinate for a pixel and its calculated value according to the underlying model. This difference is calculated:
According to our example we will determine the difference between 40.7° and 40.2°:
Due to the fact that the number of addends (or points used for the georeferenctiation) will influence the model most people tend to keep the number small. Nevertheless an increased number of reference points will provide a better model and can decrease the RMSE as well. It works the same way like the regression or correlation analysis: if the number of objects is small the model is saying nearly nothing.
An increased number of objects will increase model reliability and therefore quality of your work. In the process of the georeferenciation you will increase the reliability of your output coordinates in the map and your overall accuracy of your further work.
Katika GIS yangu ya kwanza-course mimi aliwasilishwa na dhana ya vipimo ubora kwa ajili ya georeferencing. "Mwalimu wangu alinifundisha: ""Hapa ni: RMSE au msingi wastani mraba makosa." Kumbuka: ni lazima si kubwa kuliko nusu ya ukubwa pixel! <unk> Hiyo ilikuwa yote mimi got. Kama wewe inakabiliwa na maneno sawa kushikamana na sisi! RMSE ni njia ya kupima umbali wa data inayotarajiwa na data iliyopimwa. [Q] Ambapo ni uhusiano na georeferencing picha? [A] Kama wewe ni kufanya georeferenciation wewe ni kutumia mfano wa kufanya mpito kutoka pixel-msimamo katika picha yako kwa kuratibu kijiografia. Mfano huu ni kuhesabu data inatarajiwa. Kama mfano sisi kushikamana na affine makadirio ya picha. Affinity projection ni pamoja na tafsiri, compression, upanuzi, mzunguko na mchanganyiko wa haya. Ili kuweka rahisi zaidi, sisi kuzingatia sasa tu juu ya pixels katika mwelekeo wa x (longitudes): Bila shaka mfano linear au uhusiano kati ya idadi ya pixels na longitudes ni <unk>already<unk> kufafanuliwa na pointi mbili (kama unataka kuhamisha hii kwa x na y maadili: affine mabadiliko (polynomial mabadiliko ya daraja la kwanza) ya picha ni kufafanuliwa na pointi tatu). Lakini sisi sote tunazalisha makosa: Ikiwa unatumia GCP (Vituo vya Udhibiti wa Ardhi, kama katika picha ya anga) au kuratibu za ramani: data unayotumia imejaa makosa yanayowezekana. Kupunguza makosa yako ni bora kutafuta na kutumia pointi zaidi na kufafanua yao halisi-dunia kuratibu. Lakini kufanya hivyo kutasababisha mfano mwingine. Kwa mfano, unaweza kuamua kuunganisha data yako na data ya data kwa njia ya uchambuzi wa regression, ambayo inasaidia kupunguza tofauti kati ya pikseli na thamani ya pixel kulingana na mfano wa msingi. Kwa mfano, kwa mfano, tunaweza kuamua tofauti kati ya 40.7 ° na 40.2 °, kwa sababu idadi ya addends (au pointi kutumika kwa georeferencing) itaathiri mfano. Hata hivyo, idadi kubwa ya pointi za kumbukumbu itatoa mfano bora na inaweza kupunguza RMSE pia. Inafanya kazi kwa njia ile ile kama uchambuzi wa kurudi nyuma au uhusiano: ikiwa idadi ya vitu ni ndogo mfano unasema karibu chochote. Ongezeko la idadi ya vitu itaongeza uaminifu wa mfano na kwa hiyo ubora wa kazi yako. Katika mchakato wa georeferencing utaongeza kuegemea kwa kuratibu zako za pato katika ramani na usahihi wako wa jumla wa kazi yako ya baadaye.