Fernerkundung I (Digitale Bildverarbeitung) - Friedrich-Schiller ...

1. create test sites as graphic planes in Imageworks
2. change PCIPIX file layout (add channels for results)
3. save graphic planes to new graphic bitmaps into the PCIPIX file
4. create class signatures using the graphic bitmaps
CSG
CSR
CSE
SIGSEP
SIGMER
CHNSEL
Class Signature Generator
Class Signature Report
Class Signature edit
Signature Separability
Class Signature Merging
Multispectr Channel Selection
5. Classify the image data using e.g. MLC into the empty image channels
MINDIS
MLC
Minimum distance Classifier
Maximum Likelyhood Classifier
6. Create a report of the classification and of evaluation areas for accuracy purpose
MAP
MLR
TRAIN
use MAP to burn (encode) evaluation areas (graphic bitmaps) into
an image channel with identical class codes as used for the graphic
bitmaps in the classification
use MLR to create the reports: MLR Maximum Likelyhood Report
Classification procedure from PCI v.6 (obsolete)
Unsupervised Classification:
1. Use directly KCLUS or ISOCLUS
KCLUS
FUZCLUS
ISOCLUS
NGCLUS
NGCLUS2
FUZ
(K-Means Clustering) unsupervised clustering using the K-means
(Minimum Distance) method on image data for up to 255 clusters
(classes) and 16 channels. The output is a theme map directed
database image channel.
(Isodata Clustering Program) Performs unsupervised clustering
using the ISODATA method on image data for up to 255 clusters
(classes) and 16 channels. The output is a theme map directed to a
database image channel.
8-Bit Narendra-Goldberg Clustering, maybe better clustering results
Multi-bit Narendra-Goldberg Clustering
Unsupervised Fuzzy Classification, implements an unsupervised
fuzzy clustering algorithm. A maximum of 255 clusters can be
generated from 16 image channels. Each cluster is stored into a
separate image channel. The intensity value of each pixel in each
image channel is proportional to the degree of membership of that
pixel corresponding to the channel's.
2. Merge classes or signatures and recluster the data:
AGGREG
Interactive Class Aggregation, merges up to 255 selected classes
together. Using the clustering results, the user can display classes
in different colours, group classes into aggregates, change the
colours of classes, and change classes under graphic bitmaps to
another class.