I am attempting to perform image classification on some maps that I have in PNG format.  This classification is failing due to there being too many many pixels in the training samples (I appear to be having the same problem as this user: ).  As you can see in the picture, a relatively small training area contains a huge number of pixels as shown in the count column.  The PNG image that I imported to GIS is ~4000 x ~4000 pixels.  The number of pixels in this sample is larger than the number of pixels in the entire .png image!    Is there a setting I need to change to get ArcGIS to recognize the resolution of the image for the sake of classification?
122770
I've no clue how to solve this issue in the exercise that I'm trying to perform.  The point where I'm stuck is 4. "Create signature file as LandsatCompS.sgs". I've done what it says in the exercise. I got a error message "Failed to create signature file.  The aim of the exercise is to carry out a supervised image classification of the Northern half of Jutland.   Start ArcMap and add the Landsat images from the B series (summer images). Create a composite image composite image for all bands except band 6 – the thermal band. Save the image as LandsatCompS (S for summer). Open the orthophotos for land (not urban). Add eventually more orthophotos from the Geodata library. Try to identify the necessary number of different land cover classes to cover all areas on the orthophotos. Not more than 15! Open the Image Classification window, and start digitising training areas. Two for each class. Create the signature file as LandsatCompS.gsg. Carry out maximum likelihood classification using LandsatCompS as input raster and LandsatCompS.gsg as signature file. Output is ML_class_NJ1. Use default values for the other inputs. Put the right colours on the new map and compare with the orthophoto and other maps available for Northern Jutland. Where are the problems? Try to make an improved classification by increase the number of classes and digitise new training areas. Carry out the classification and assess the result. Which one is best?
104293
When I export Illustrator artwork as a png file, I get a one pixel grey border at the top and left edges.  There is no background object, but I export (Save for Web and Devices) with matte white background and no transparency.  I also tried with a white background object, with it snapped to the artboard and also larger than the artboard by 10 pixels in each direction.  I still got the lines at the top and left of the exported image.  I also tried making a new file, with the artboard in pixel units, and selected the option to have objects snap to pixel grid, and then copying and pasting the artwork from the original file.  I still got the lines.
109234
I am attempting to fully automate the process of georeferencing a load of .png files - i have xml file containing the co-ordinates - but how can i use them?  Does anyone have any advice?
81944
I'm trying to reclassify only a certain part of a raster...not the entire raster.  Here's the problem.  I have a LULC raster of the entire US.  There are some issues with the raster that I want to fix, namely some cells that should be water are classified as wetlands.  These are mainly off the coast, not inland.  However, some cells that are classified as wetlands should really be wetlands (mainly inland) and I don't want these to change.  I plan on using a ZIP Code boundary file to determine which cells are considered "off the coast" and which ones are not.  I have access to ArcGIS 10.1 and R with the appropriate raster packages I need so a solution in either software would be fine.  Can anyone help me figure this out?
69474
Most of the advanced deep learning models like VGG, ResNet, etc. require square images as input, usually with a pixel size of $224x224$.  Is there a reason why the input has to be of equal shape, or can I build a convnet model with say $100x200$ as well (if I want to do facIAL recognition for example and I have portrait images)?  Is there increased benefit with a larger pixel size, say $512x512$?
105857
In developing a relatively simple scene, I noticed that detail from normal maps were not being displayed for some objects. All of the maps were generated from Substance painter as .png (16-bits max).  Further investigation in the console showed that loading the normal maps was being skipped due to a float image limit of 5 textures.  Through Google, I found bug report T37264 which indicated that this was due to a limitation in CUDA.  Indeed, switching the render from GPU to CPU cleared the problem (even in the viewport render, which was surprising).  Also, the problem went away by using .jpg for the normal map textures.  So, my questions are: Are these steps the only solution for this problem?  I was not aware that I was even using float textures.  Is .png acutally a floating point format?  What about the other .png maps used for color data--are they also count against the floating point texture limit?  Edit to add: I found that 8-channel .png works ok, so I guess that would be my lossless solution.
105346
I've come across a scenario where I have 10 signals/person for 10 people (so 100 samples) containing 14000 data points (dimensions) that I need to pass to a classifier. I would like to reduce the dimensionality of this data and PCA seems to be the way to do so. However, I've only been able to find examples of PCA where the number of samples is greater than the number of dimensions. I'm using a PCA application that finds the PCs using SVD. When I pass it my 100x14000 dataset there are 101 PCs returned so the vast majority of dimensions are obviously ignored. The program indicates the first 6 PCs contain 90% of the variance.   Is it a reasonable assumption that these 101 PCs contain essentially all the variance and the remaining dimensions are neglectable?  One of the papers I've read claims that, using a similar (though slightly lower quality) dataset than my own, they were able to reduce 4500 dimensions down to 80 retaining 96% of the original information. The paper hand-waves over the details of the PCA technique used, only 3100 samples were available, and I have reason to believe less samples than that were used to actually perform PCA (to remove bias from the classification phase).   Am I missing something or is this really the way that PCA is used with high dimensionality-low sample size dataset? Any feedback would be greatly appreciated.
33271
I am looking for a method to process a remote sensing image and extract the crown areas of the individual trees from the image.   I have both visual wavelength areal imagery, and lidar data from the area. The location in question is a desert area, so the tree cover isn't as dense as a forest area. The resolution of the aerial imagery is 0.5 feet by 0.5 feet. The lidar resolution is approximately 1 x 1 feet. Both the visual data and the lidar come from a Pima County, Arizona dataset. A sample of the type of aerial imagery I have is at the end of this post.   This question  seems to be the same issue, but there does not seem to be a good answer there.  I can obtain a reasonable classification of the vegetation types (and information about the overall percent cover) in the area by using the Iso Cluster classification in Arcmap, but this provides little information on individual trees. The closest I have to what I want is the results of passing the output of the isocluster classification through the Raster to Polygon feature in Arcmap. The problem is that this method merges near by trees into a single polygon.   Edit: I probably should have included some more detail about what I have. The raw datasets I have are:    Full las data, and a tiff raster generated from it. Visual imagery (like the sample image shown, but covering a much wider area)  Manual direct measurements of a subset of the trees in the area.    From these I have generated:    The ground/vegetation classifications. The DEM/DSM rasters.
47128
I work with people who use ArcGIS Desktop and they have found the best resolution output images are made by creating PDFs. They tell me that *.png and the rest out of ArcGIS Desktop are not satisfactory and that they do not bother with them. Typically we are making the output appear in a report and so the PDFs of the maps and words are assembled in a PDF editor package to make the final document.   As a result I have been toying around with QGIS on my Windows 7 machine and notice that the best output comes by printing directly out of the map composer.  This output on my Docuprint C2120 printer is as good as any of the ArcGIS Desktop PDFs and is truly impressive. QGIS makes very attractive maps in this configuration.   However, I do not seem to be able to create PDFs or .pngs and then print them which are anywhere near as good. I find the .svg output to Inkscape unrealiable where, for instance, the grid/graticule may come out oversized.   By way of example, the good quality output is where a sub-6 pt font can be read easily off the printed page.   Any thoughts?  I am using QGIS trunk.
73224
I want to convert a batch of images, nearly 100, from jpg to png format. How can I do this without renaming them, but instead actually converting the format?
123008
I was sent an image (.psd) that has transparent background. When I export it the resulting .png preserves the transparent background so the image actually takes more space on a website than necessary. It's a logo image.  I'm trying to adjust the transparent canvas site to only take the space by the image itself.  Alternatively, how do I export it so that the resulting .png is only the size of actual logo (rectangle) not including the transparent space (bigger rectangle).  Thank you.
5590
I have a geo-tiff created by Pix4D which is about 375 Mb in file size.   I have clipped it to a smaller area using Qgis 2.8(using Raster ‣ Extraction ‣ Clipper), and the output file is about 900 MB in size.  Why did the clipping process generate such a large file, when the source file was smaller?   How do I run the clipper tool so that it produces a smaller size?
112447
The same problem over and over again - why do images placed in Id/Ai show different size than in PS?  I've made image in PS of about 2000x1500 pixels at 300 PPI.  Then made Ai document of 2500x1600 pixels at 300 PPI. To avoid any conversion/compression surprises I simply placed the Ps document in Ai document. Guess what? It's shrunk and terribly pixelated though MB size in link properties seems correct. Ok, then maybe for some reason it reduced the resolution: let's make an Ai doc at 72 PPI. Surprise again - the placed PS doc is exactly the same size as it was in 300 PPI doc. And exactly the same pixelated.  Let's try Id then: one with print intent (so it should have like 300 PPI) and one with web intent (should have about 72 PPI), both size like before. Guess what? Absolutely no change - Ps doc is shrunk and pixelated (and appears same size in both cases), though shows correct MB size, Actual PPI = Effective PPI = 300 and correct dimensions according to link description.  When tried with *.png file instead of Ps, nothing changed as well.  I'm totally dumbstruck by now. How is it that suddenly one 300 PPI file is not working with other 300 PPI file and 2000pix height becomes less than 1/5 of 2500pix height?...
110044
I have downloaded raster map data from ONS, the data has come in the form of two files... one is full of  .tlw files and the other is full of .tab files...   How do I import it into a raster layer to ArcCatalog?
96587
Using ArcGIS Desktop v10.2 with an Advanced level license we have georeferenced our engineering drawings in PNG format. Next we have created a polygon layer for every single drawing to see the Area of Interest depicted in the drawing. Is there an automated way to bring individual PNG layers by clicking on their respective polygon?  I want a simpler way to add the georeferenced PNG into the map without having to use the classic "add layer" tool, where a user has to find the respective layer in their location on the network.
83277
I'm looking for recommendations on best practices for dealing with raster data layers with different resolutions and projections. The advice I've been given is to always resample to the layer with the lowest resolution before performing any analysis, but this seems like huge waste of precision to me and I've never been given a solid explanation for why it should be done.  When is it reasonable to resample to match a higher resolution grid and what are the implications compared to resampling to a lower resolution?  I realize that this is likely highly situation dependent. I'm mostly looking for general guidelines, but here's my specific scenario for reference:  Scenario: I'm looking to construct a spatial regression model predicting land use based on a variety of environmental and socio-economic layers. My land use map is Landsat derived and hence 30m resolution. Examples of explanatory layers include the  (3 arc-seconds, ~90m) and  climate layers (30 arc-seconds, ~1km).
44385
I am using Windows 7 enterprise and QGIS version 2.12.2 Lyon.  Have found an unexpected result when I use QGIS to generate a topographic position index (TPI) raster from a 30m by 30m DEM.  I find a pattern of what I believe to be aberrantly valued pixels forming a square grid pattern that are approximately 900 m on each side.  I expected the resulting raster to not have this grided appearance.  UPDATE: I have looked at my hillshade, slope, aspect, terrain ruggedness index and roughness rasters generated using the same tool and they all have this same "grid" (although it is much less apparent than in the TPI raster).  Here's a screenshot of the original DEM I used:   Here's a screenshot of the resulting TPI.  Notice the grid pattern formed over what I suspect to be an accurate TPI raster layer.   Here's how I created the TPI Raster: Raster – analysis – DEM: