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Arc GIS have introduced the image classification tool in version 10. If You want to classify the images using Arc Map. Check this tutorial of the tool. You may also use other softwares like ENVI or ERDAS IMAGINE for this purpose. For the case of ENVI this tutorial guides you through the process and can perform both supervised and unsupervised ...


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I would hazard to guess that your Professor is asking that your classification be done at a finer resolution than you've presented here. If the image you show is at the same cell size as the original image then changing the resolution does not change the fundamentals of the classification. If you reduced the resolution from the original image to process ...


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You should check your Environment Settings and adjust the Cell Size to an appropriate cell size: ArcGIS Cell Size


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Maximum Likelihood Classification, in any remote sensing software, will consider all of the bands passed to the tool and not be limited to the RGB spectral space. However, one thing to keep in mind is that Maximum Likelihood does not do very well with data in different scales so, for the best results, you want to match the bit-depth of your data. For ...


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You can add offsets to avoid overlapping symbols: Add the layer twice as suggested by @iant♦ When defining the style you can go to the Symbol selector for each symbol and set an offset for it. Set desired offset here Duplicated layer with point symbols and offset


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You can use Data defined override in combination with categorised symbology. With this approach you can do categorised symbology on one attribute and set some other parameter (line style, line width etc.) to change according to other attributes. example on OSM road data: classified by attribute "type" (line color) change the symbol and choose some ...


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in the absence of NIR band, you can try the following index because the blue component is relatively high with respect to the red for most swimming pools. (blue-red)/(blue+red) (Borja Rodríguez-Cuenca and Maria C. Alonso, Remote Sensing, 2014) however, there might be confusions with shadows, therefore you need to predict shadow position (if you have a ...


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Probably not easy as the reflectance of water is pretty special and is easy detected because of the drop in reflectance in the NIR-Band. The only chance is to detect low reflectance in general. But swimming pools often reflect in the color of the material of which they are made of because they are shallow and have clean water. You can try supervised ...


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In ArcMap 10.4, there is the ClassifyLASGround tool, from the 3D Analyst toolbox. According to ESRI this tool: Classifies ground points in lidar data. Only the last return of LAS points with class code values of 0 (never classified), 1 (unassigned), or 2 (ground) will be considered for reclassification as ground. It has 3 methods for performing the ...


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There are various algorithms developed for classifying a LiDAR point cloud. ArcGIS doesn't have this directly implemented (up to version 10.1 at least), but I recommend lastools. It's a collection of packages for LiDAR processing, including bare earth classification (the module is named lasground). They even have toolboxes developed for import in ArcGIS, so ...


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McNemar's test is a test for paired proportions, I do not see how it applies to a multi-class confusion matrix. Commonly, it is applied to validate logistic models. You cannot hope to aggregate the entire confusion matrix into an 2x2 contingency matrix and expect a valid hypothesis test. I suppose that you could iterate through classes, deriving an ...



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