#include <tcl.h>
#include <tk.h>
#include <stdio.h>
#include <string.h>
#include <sadie.h>
#include "tclsadie.h"
#include "Sadie_Index.h"
#include "tclSadie_ROI.h"
Go to the source code of this file.
Functions | |
int | Sadie_Classify_LvlSliceCmd (ClientData client_data, Tcl_Interp *interp, int objc, Tcl_Obj *const objv[]) |
Level-slice classifier (box classifier). | |
int | Sadie_Classify_MinDistCmd (ClientData client_data, Tcl_Interp *interp, int objc, Tcl_Obj *const objv[]) |
Minimum distance image classification, with several distance metric options. | |
int | Sadie_Classify_MaxLikeCmd (ClientData client_data, Tcl_Interp *interp, int objc, Tcl_Obj *const objv[]) |
An interface to the MAXLIKE function in the SADIE library (not working). | |
int | Sadie_Classify_ClusterCmd (ClientData client_data, Tcl_Interp *interp, int objc, Tcl_Obj *const objv[]) |
Image classification by an iterative K-means clustering algorithm. | |
int | Sadie_Classify_SegmentCmd (ClientData client_data, Tcl_Interp *interp, int objc, Tcl_Obj *const objv[]) |
Segment an image into regions with similar greylevels. | |
int | Sadie_Classify_SigMapCmd (ClientData client_data, Tcl_Interp *interp, int objc, Tcl_Obj *const objv[]) |
Assign realistic mean values to an arbitrary label map. | |
int | Sadie_Classify_Init (Tcl_Interp *interp) |
Make the commands defined in this file accessible to the Tcl interpreter. | |
Variables | |
sad_doclistp_t | global_doclist |
The global hash table structures, used for ROI storage. |
The SADIE library implements some of the classification operations described in Chapter 9 of R. A. Schowengerdt Remote Sensing (2nd. ed.) Academic Press: 1997. In addition to the images themselves, they require definitions of regions of interest, which are recorded neither within the SADIE library, nor within the Tcl code, but within an independent hash table (see Sadie_Index.c for the implementation details).
Definition in file Sadie_Classify.c.
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Image classification by an iterative K-means clustering algorithm. This is simply an interface to the SADIE library function CLUSTER. Starting with a certain number of arbitrarily defined clusters, we classify each point by assigning it to the cluster whose mean vector is closest in feature space. We then re-evaluate the cluster mean vectors and iterate the process, re-assigning points between the clusters. Adjustable parameters allow clusters whose means become close to merge, and clusters that end up holding too few points to vanish. The Tcl data should conform to the patterns:
Definition at line 595 of file Sadie_Classify.c. References GetSadieImageFromObj2(), GetSadieIntFromObj2(), GetSadiePixelFromObj2(), and SetSadieImageObj2(). Referenced by Sadie_Classify_Init(). |
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Make the commands defined in this file accessible to the Tcl interpreter.
Definition at line 721 of file Sadie_Classify.c. References Sadie_Classify_ClusterCmd(), Sadie_Classify_LvlSliceCmd(), Sadie_Classify_MaxLikeCmd(), Sadie_Classify_MinDistCmd(), Sadie_Classify_SegmentCmd(), and Sadie_Classify_SigMapCmd(). Referenced by Tcl_AppInit(). |
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Level-slice classifier (box classifier). The LVLSLICE function in the SADIE library performs a simple level-slice classification, but needs additional help computing the level ranges for each band and class from the mean vectors of a set of training areas; Note that "the level-slice classifier does not fit remote-sensing data particularly well." (R.A. Schowengerdt Remote Sensing (2nd. ed.) Academic Press: 1997: p. 412). The Tcl data should conform to the patterns:
Please refer to set_class_summary_vec for details of the Tcl classification array.
Definition at line 160 of file Sadie_Classify.c. References GetSadieDkeyFromObj2(), GetSadieImageFromObj2(), GetSadieIntFromObj2(), GetSadiePixelFromObj2(), sad_get_doc_property(), SetSadieImageObj2(), and thresh. Referenced by Sadie_Classify_Init(). |
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An interface to the MAXLIKE function in the SADIE library (not working). The MAXLIKE function should provide a Bayesian probabilistic image classification, but at the moment is just a placeholder. Definition at line 445 of file Sadie_Classify.c. References GetSadieBooleanFromObj2(), GetSadieDkeyFromObj2(), GetSadieDoubleFromObj2(), GetSadieImageFromObj2(), GetSadieIntFromObj2(), sad_get_doc_property(), and SetSadieImageObj2(). Referenced by Sadie_Classify_Init(). |
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Minimum distance image classification, with several distance metric options. Given a set of regions within an image, a number of classes (not greater than the number of regions), an assignment table that shows which regions belong to which class, and a complete unclassified image, this uses the SADIE library function MINDIST to generate a classified image, where the class assigned to each pixel is the one whose mean vector is closest to the corresponding point in the unclassified image. The "closest" criterion is the minimum distance in feature space, according to a particular distance metric, and there are several options for this metric (including Euclidian, city block, and Mahalanobis). The Tcl data should conform to the patterns:
The Tcl classification array follows the pattern
Definition at line 310 of file Sadie_Classify.c. References GetSadieBooleanFromObj2(), GetSadieDkeyFromObj2(), GetSadieImageFromObj2(), GetSadieIntFromObj2(), sad_get_doc_property(), and SetSadieImageObj2(). Referenced by Sadie_Classify_Init(). |
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Segment an image into regions with similar greylevels. As well as assigning labels to groupings of similar pixels, this will optionally downsample the image before a coarse segmentation, then refine the labelmap to the resolution of the original image: see the SADIE library SEGMENT and REFINE functions for details. The Tcl data should conform to the patterns:
Definition at line 648 of file Sadie_Classify.c. References GetSadieDoubleFromObj2(), GetSadieImageFromObj2(), GetSadieIntFromObj2(), GetSadiePixelFromObj2(), SetSadieImageObj2(), and thresh. Referenced by Sadie_Classify_Init(). |
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Assign realistic mean values to an arbitrary label map. Given an image and a label map defining regions within it, generate a new image, assigning values to all the pixels within a particular region the mean value from the corresponding region in the original image, using the SADIE library SIGMAP function. The Tcl data should conform to the patterns:
Definition at line 693 of file Sadie_Classify.c. References GetSadieImageFromObj2(), and SetSadieImageObj2(). Referenced by Sadie_Classify_Init(). |