Leaderboard

there is also a new tool in arcgis pro called "Pixel Editor" if you need to correct the values for this tool you need the Image analyst license. https://pro.arcgis.com/en/pro-app/help/analysis/image-analyst/editing-elevation-pixels.htm depending on your task or your licence you can also use the "Raster Calculator" (needs spatial analysis). For example if you have a polygon with the elevation data and you want to substitute the values on your raster (polygon to raster, then use the conditional function > con() https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/con-.htm

hi, why do you use landsat 7 and not landsat 8? and with witch software? here you can see the band combination for landsat 7 and 8 http://landsat.usgs.gov/L8_band_combos.php http://web.pdx.edu/~emch/ip1/bandcombinations.html so the band combination for TM is 1-4-7 in Landsat 7, the combination can be 7-6-4 http://www.harrisgeospatial.com/company/pressroom/blogs/tabid/836/artmid/2928/articleid/14305/the-many-band-combinations-of-landsat-8.aspx you load every single band in ArcGIS and you do a combination then do a classification (supervised or unsupervised) Two major categories of image classification techniques include unsupervised (calculated by software) and supervised (human-guided) classification. Unsupervised classification is where the outcomes (groupings of pixels with common characteristics) are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as wetlands, developed areas, coniferous forests, etc.). Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Training sites (also known as testing sets or input classes) are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the training area, plus or minus a certain increment (often based on "brightness" or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into. Many analysts use a combination of supervised and unsupervised classification processes to develop final output analysis and classified maps. (source : https://articles.extension.org/pages/40214/whats-the-difference-between-a-supervised-and-unsupervised-image-classification)