PSCPS. See Point simple cokriging
point cosimulation (PSCPS)
Pyramids, 146
QuickBird data for, 27
Quadtrees, 146
QuickBird, 13, 82, 133, 302
data, 27, 133
comparison of various segmentation
algorithms on, 189
Digital Globe, 82
experiment, 201
image, 187, 189, 201, 203
acquisition and preprocessing, 130
atmospheric and geometric
corrections, 134
optimal scale value in multiresolution
segmentation using, 203
resolution, 135
as satellite imagery, 65
lower classification accuracy, 201
Radar remote sensing, 27
Radar satellite imagery, 186–187
Radiative transfer estimates, 50
Random access memory (RAM), 157
Random-number generator, 112
Ranging (lidar) system, 267
Regionalization, 3
Region-based image segmentation algorithm
(RISA), 203
data, 206
Regression modeling, 108
Remote measurement discrepancy, 287
Remote sensing, 141
data, 198
sensors, 172, 218, 311–312
technology, 73
Resolutions, 3, 223, 224, 231
coarse, 5
geometric, 14
spatial, 4, 8, 65, 81, 218, 220, 234, 239,
242, 250, 302, 303
spectral, 310
temporal, 81, 256, 303
thematic, on landscape pattern
analysis, 5
Reversed J-shape distribution, 111
River dynamics, 267
Satellite images, 8, 82
high-spatial-resolution, 61
use of aerial photographs, 64
Satellite rainfall estimation error, 253
precipitation data sets (PERSIANNCCS), 263
Satellite remote sensing data, 197
Satellite sensors, 3, 9, 14, 18, 22, 86, 127,
286
Scale analyses, 68
Scale dependent, 68
Scale domains, 142
manifolds, 143
multiscale analysis, 148
scale-space events, 143
visualization, 142
Scale ratios, 6
Scale-related issues, 5
Scale space (SS), 142
events and domain thresholds, 160–162
filtering (See Scale-space filtering)
Gaussian operators, 149–151
integrating hierarchy theory, 156–157
to reduce processing
requirements, 156–157
lifetimes to scale domain-level
topology, 158–159
linear, 143, 148–149
representations (See Discrete-return lidar
system)
shared-event classes, 159–160
colorized model, extracted from,
161–162
modified “scaling ladder,” 162
stack, 176
uncommitted framework, 149
Scale-space filtering, 177
anisotropic diffusion methods, 177
anisotropic morphological levelings
(AML), 177
computer vision applications, 177
nonlinear scalespace representation,
construction of, 177
region merging segmentations result,
comparison of, 178–180
scale-space cube (3D) representation, 177
Scale-space image representations, 7, 173,
178–180
AML formulation, 177
332
INDEX
point cosimulation (PSCPS)
Pyramids, 146
QuickBird data for, 27
Quadtrees, 146
QuickBird, 13, 82, 133, 302
data, 27, 133
comparison of various segmentation
algorithms on, 189
Digital Globe, 82
experiment, 201
image, 187, 189, 201, 203
acquisition and preprocessing, 130
atmospheric and geometric
corrections, 134
optimal scale value in multiresolution
segmentation using, 203
resolution, 135
as satellite imagery, 65
lower classification accuracy, 201
Radar remote sensing, 27
Radar satellite imagery, 186–187
Radiative transfer estimates, 50
Random access memory (RAM), 157
Random-number generator, 112
Ranging (lidar) system, 267
Regionalization, 3
Region-based image segmentation algorithm
(RISA), 203
data, 206
Regression modeling, 108
Remote measurement discrepancy, 287
Remote sensing, 141
data, 198
sensors, 172, 218, 311–312
technology, 73
Resolutions, 3, 223, 224, 231
coarse, 5
geometric, 14
spatial, 4, 8, 65, 81, 218, 220, 234, 239,
242, 250, 302, 303
spectral, 310
temporal, 81, 256, 303
thematic, on landscape pattern
analysis, 5
Reversed J-shape distribution, 111
River dynamics, 267
Satellite images, 8, 82
high-spatial-resolution, 61
use of aerial photographs, 64
Satellite rainfall estimation error, 253
precipitation data sets (PERSIANNCCS), 263
Satellite remote sensing data, 197
Satellite sensors, 3, 9, 14, 18, 22, 86, 127,
286
Scale analyses, 68
Scale dependent, 68
Scale domains, 142
manifolds, 143
multiscale analysis, 148
scale-space events, 143
visualization, 142
Scale ratios, 6
Scale-related issues, 5
Scale space (SS), 142
events and domain thresholds, 160–162
filtering (See Scale-space filtering)
Gaussian operators, 149–151
integrating hierarchy theory, 156–157
to reduce processing
requirements, 156–157
lifetimes to scale domain-level
topology, 158–159
linear, 143, 148–149
representations (See Discrete-return lidar
system)
shared-event classes, 159–160
colorized model, extracted from,
161–162
modified “scaling ladder,” 162
stack, 176
uncommitted framework, 149
Scale-space filtering, 177
anisotropic diffusion methods, 177
anisotropic morphological levelings
(AML), 177
computer vision applications, 177
nonlinear scalespace representation,
construction of, 177
region merging segmentations result,
comparison of, 178–180
scale-space cube (3D) representation, 177
Scale-space image representations, 7, 173,
178–180
AML formulation, 177
332
INDEX