Tag Info

New answers tagged

3

Think of the geometry. The incidence angle refers to the angle from nadir, or directly beneath the satellite, which would be 0°. As the sensor looks out to the sides from this nadir, the angle of incidence increases as does the fov (field of view). This is why the resolution decreases with increase in incidence angle. This illustration from the Sentinel ...


0

It is enoying that Pan doesn't work anymore during digitizing. A workaround for the problem is: Zoom out of the image using the mouse wheel until you see your entire image, then move your arrow to the area where you want to continue your digitizing (but don't click) and zoom in again using the mouse wheel.


1

You are probably also getting more backscatter because there is bare rock at the summit instead of vegetation. Vegetation holds moisture, which would absorb microwaves rather than backscattering. Besides, the foreshortening which affects the fore slope increases its intensity (having more backscatter returned into the same cell yield more intensity + Double ...


0

http://www.tracerelectronicsllc.com/page4/tracer/locators.html The above link has lots of utility locating equipment. Gas lines will definitely require this. Water/WW utilities can usually be located pretty accurately using a RTK gps unit to first locate the valve, manhole, meter spots, and using building/construction/site plans, to draw in the connecting ...


1

I am a damage prevention specialist, and I use the receiver and transmitter equipment to locate all buried utilities. I went thru a small village 2 years ago and located all residential water services because these are private and not marked out when 811 is called. To map for the village, I utilized a Trimble Geo to create a map file for them for all future ...


0

A high number of bands does not make a sensor "hyperspectral", although the hyper could suggest that "many" is the solution. However, the typical feature of hyperspectral data is that it results in a continuous curve of reflectance across the range of wavelengths measured. Therefore, the spectral interval has to be narrow, much narrower than for the ...


1

According to NASA, a spectral radiometer is a multispectral sensor. Spectroradiometer—A radiometer that measures the intensity of radiation in multiple wavelength bands (i.e., multispectral). Many times the bands are of high-spectral resolution, designed for remotely sensing specific geophysical parameters Perhaps you're thinking of a spectrometer, ...


1

Depends on satellite, but most of them have one or two sensors with the same angle of view. For example, Landsat 8 has 2 sensors, TIRS (2 bands) and OLI (9 bands). All of bands have the same angle while sensing. Figuratively speaking - you can not sense Africa with 4 OLI bands and at the same time sense Europe with other 5 bands.


0

It seems to me that many satellites are programmed to go over a certain area at the same time and in the same pass. This increase coherence between each pass's images, which facilitate operations such as change detection on the image series generated from the set of images collected from the different passes.


1

extract pulls raster values out based on an intersection with a vector. That could be helpful if you wanted to sample raster values given something like a points shapefile. In this case, however, you want a to identify the cells based on value, and then get the data directly from the raster. This may need to be modified if performance in reading the data is ...


3

Reading the images works with the gdal palsar driver and therefore also in Python. You have to make sure to point gdal to the VOL file so it works. >>> palsar = gdal.Open("D:\Downloads\psr_fbs15\PSR_FBS15\VOL-ALPSRP150170690-H1.5_UA") >>> pal_arr = palsar.ReadAsArray() >>> type(pal_arr) <type 'numpy.ndarray'> Saving in ...


2

The look direction of the C-SAR instrument is right. The resolution as well as the pixel spacing depends on the product and the acquisition mode. They can range from 1.7m x 4.3 for Level 1 SLC SM to 52m by 51m for Level 1 GRD WV. ESA provides a list of all resolutions and pixel spacings for Sentinel-1 products.


3

It depends upon the intended use of the Landsat data. Generally speaking, if you are doing multi-temporal analyses, you need atmospherically corrected data, otherwise DN format is sufficient. I would recommend reading the following landmark paper on the subject: Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). ...


0

Draw polygons with ArcGIS. The polygons are areas with a specific type of vegetation. You have some areas FROM Landsat 7 and MODIS. You want to "export" the spectral signature of these areas. With ArcGIS you can use "zonal statistics as a table" to extract the mean spectral values for each band and within your polygon. You can run it in batch mode (right ...


0

Well, thank you so much @Aaron. The problem has solved. I have realized that the above code needs to be slightly modified for the case of Google Earth imagery regarding its conversion to grey-scale intensity image. Following is the output of my modified code. Impervious surfaces of building rooftops have been extracted. Nevertheless, the results can be much ...


1

I have also had a lot of success using the image classification tools in ArcGIS (http://resources.arcgis.com/en/help/main/10.2/index.html#//00nv00000008000000). The documentation is great and the results have been very accurate. Unsupervised classification is tricky because defining the number of classes will always result in some degree of mixing. Even ...


1

You can use the OpenCV package in Python for image thresholding. This example shows not only how to perform the binary image thresholding, but also the limitations of this method. Here, I use a 1m spatial resolution NAIP image that shows a dirt road surrounded by arid vegetation. You can see that the road is extracted but there is also a significant amount ...


1

If you use QGIS, there is a plugin named "semi automatic classification". It may not take too much time to utilize the plugin because you might be familiar with the RS analysis methods. I have used it for 1 week and have been pleased. The plugin is also capable of downloading landsat's photos. Here's the link of classification tutorial. ...


5

I hacked together a solution for this and wrote a blog article a while back on a very similar topic, which I will summarize here. The script is intended to extract a river from a 4-band NAIP image using an image segmentation and classification approach. Convert image to a numpy array Perform a quick shift segmentation (Image 2) Convert segments to raster ...


3

You could look at clustering in scikit-learn. You will need to read the data into numpy arrays (I'd suggest rasterio) and from there you can manipulate the data so that each band is a variable for classification. For example, assuming you have the three bands read into python as red, green, and blue numpy arrays: import numpy as np import sklearn.cluster ...


0

As far as i know there's no way of performing atmospheric correction for L8 images in Arcgis 9.3. but there's an plugin i QGIS called Semi-Automatic Classification that will perform DOS on Landsat 8 images. Check it out, it's pretty good. You can find all the information here: http://fromgistors.blogspot.com/ and also in the Semi-automatic Classification ...



Top 50 recent answers are included