1 pointyou must built your own RPC from GCP using ENVI, ERDAS, PCI Geomatica and few others image processing software, . Keep in mind that the accuracy of derived RPC heavily relied on your GCP number, accuracy and distribution. If you are using GeoEYE imagery at <0.5 meter resolution, basically you need GCPs derived from Geodetic GNSS survey (static/kinematic) which has accuracy <10 cm.
1 pointCactuz, Decision rules are arbitrary unless they are derived using a statistical function like recursive binary partitioning. There's not a one-size-fits-all rule for specific cover types. The more well known programs for classification and regression trees (CART) for remote sensing purposes are See5 and Cubist by RuleQuest. Idrisi has a built-in Decision Tree classifier dating back to Andes edition. If you are using ENVI, you can apply some of the built-in non-parametric classifiers such as Support Vector Machine (SVM) or Neural Net. These are machine learning algorithms capable of handling both continuous and categorical data. If you have your mind set on decision trees, then you can explore some of the free and good options. A developer on Google Code implemented C45 (free source code of See5) in IDL. This allows you to create decision rulesets using the See5 approach and the output is in the ENVI Decision Tree format. Just choose the option in ENVI > Classification > Decision Tree and "Execute Existing Decision Tree." I've used this program myself and the results are impressive. The developer created a decent GUI if you aren't comfortable with IDL command line. You can find the code here: https://code.google.com/p/c45idl/ However, my favorite free program is the R implementation of RandomForests. This is an extremely robust classifier. It requires knowledge of R, plus dependent libraries. You can download the randomForests script here: https://bitbucket.org/rsbiodiv/randomforestclassification ....and there are installation instructions and tutorials here: https://bitbucket.org/rsbiodiv/toolsforr http://www.whrc.org/education/indonesia/pdf/DecisionTrees_RandomForest_v2.pdf Good luck
1 pointSure. 1: Make sure you have several ground control points (5 per image, distributed around the picture, ought to do it although if you have very hilly terrain then you should use more...) with XY (use an ortho image) and Z (use an existing DEM) (alternatively you can go out in the field and measure it yourself) where the ground control points are on ground level (i.e. a road intersection, not a building). 2: Use LPS in ERDAS to load the stereo imagery in and then ground reference away. 3: Automatically generate tiepoints and check up on RMSE values (that they are not too high, otherwise you will have verify that the ground control points are on the correct location) 4: After this, you can generate a DSM and adjust it with the eATA or aATA software to filter, adjust and view the points in ERDAS. These points can then be exported to a contour, TIN or Raster. Good luck!