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on the problem considered, an analyst can favor one or |
another. However, there are some guidelines that should be taken into account that will depend mainly on the data |
available (e.g., the sensors to be used) and on the effort |
already provided by the analyst (e.g., whether a classifier is already available or if labels in the target domain are |
available or can be acquired easily). The guidelines are |
as follows: ◗If the data to be used are acquired by different sensors, they are associated with different feature spaces. In this |
case, only heterogeneous DA (i.e., methods that allow |
for aligning spaces of different dimensionality) should be considered. Accordingly, multiview feature-representation |
transfer methods such as CCA and KCCA or MA (see the |
“Adapting Data Distributions” section) are the recom - |
mended choices. |
◗If a classifier trained on the source is already available and the effort of training is considerable, methods of |
the third and fourth families (i.e., the adaptation of |
classifiers and the adaptation by selective sampling, respectively) should be preferred. These methods |
build on the model that is already defined on the |
source domain, while those of the two other families imply the definition of a new classifier that is success - |
ful in all domains. |
◗Whenever it is possible to acquire new labeled samples in the target, it should be done. There is no better way |
to correct for a data set shift than by having examples of |
the class-conditional distribution in the target. The AL and MA methods are to be preferred in that case. |
◗The level of data set shift the methods can cope with goes along with their level of flexibility. Representation |
methods relying on labeled samples from the target can |
cope with strong nonlinear deformations because they allow for a kind of feature registration between the do- |
mains, while those that do not use target samples (e.g., |
PCA, TCA, and GM) are successful only if the data distri-butions are already prealigned and have not undergone |
drastic shifts, such as the cases where the signature of a |
target class becomes identical with one of the others in the source. Among the unlabeled methods, the differenc- |
es in their flexibility should be considered, going from 500 550 600 650 700 750 800 8506065707580859095 |
Number of Pixels in Training SetOA (%)Source Labeled |
Target Labeled |
Random Sampling |
TrAdaBoost |
Traditional AL |
TrAdaBoost + AL |
0 5 10 15 20 25 30 350102030405060708090100 |
AL Iteration |
(a) (b)Alphas (%)% Source Norm. Alphas > 0.02 |
% Target Norm. Alphas > 0.2 |
Figu Re 10. The AL results over the KSC data of Figure 9 (adapted from [67]). (a) The learning rates (OA) for different methods. (b) The |
percentage of source (dashed red line) and target (solid blue line) a weights larger than 0.02 (source) and 0.2 (target) along the iterative AL |
process in the TrAdaBoost + AL experiment, which is shown by the black line in (a). |
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ieee Geoscience and remote sensin G ma Gazine june 201654 |
linear global methods (e.g., PCA representation transfer) |
to local methods (e.g., based on clustering, as with GM |
and MA). If the first can address only rotations, transla- |
tions, and to some extent scalings of the data clouds, the others can model the per-sample transformation and al- |
low more flexibility of the transform. The same type of |
reasoning holds for semisupervised methods, which will be able to correct for smaller shifts than methods based |
on AL. When deployed in a DA setting, AL methods can |
collect target labeled samples that provide evidence of the real target class distributions, while the semisuper- |
vised method uses only unlabeled data in the target and, |
therefore, cannot easily discover drastic changes in the class distributions. |
◗A combination of methods from different families is also possible. For example, selecting invariant features |
can be a preprocessing step to kernel MA, where the la - |
bels in the target domain have been acquired by AL us - |
ing the labels from the source domain. |
With these simple guidelines in mind, the analyst can select |
the most appropriate strategy (or combine a series of them) according to the considered data and application. |
HOW TO VALIDATE |
A typical bottleneck for the employment of an adaptation |
strategy is the validation of the adaptation process itself, |
since it is assumed that no (or only few) labeled data are available for the target domain. Nonetheless, one should |
assess whether the adaptation was successful in the pro - |
cessing of the target image, even though no labeled sam - |
ples are available for such validation. To address this |
crucial issue, a circular vali - |
dation strategy is presented |
and applied to remote sens - |
ing images in [ 64]. The strat - |
egy is based on the idea that an intrinsic structure relates |
the solutions that are consis - |
tent with the source and the |
target domains. A solution |
for the target domain, for which no prior information |
is available, is assumed to be |
consistent if the solution to the source-domain data is as - |
sociated with an acceptable accuracy. The solution to the |
source-domain data should be obtained by applying the |
same DA algorithm in the reverse sense, i.e., by using the |
classification labels in place of missing prior knowledge for target-domain instances. The source-domain data is |
considered as unlabeled in the reverse DA learning, and |
the accuracy of the source-domain data can be evaluated due to the available true labels for source-domain sam - |
ples. This strategy can be effective for both understanding if the adaptation is feasible in the considered data set and |
selecting the most effective strategy. |
conc LuSionS |
In this article, we reviewed the recent DA advances for re - |
mote sensing image analysis. DA is a rising field of investi - |
gation in remote sensing, as it answers the need for reusing |
available ground reference samples to classify or further |
process new image acquisitions that may be covering dif - |
ferent areas, at different time instants, and possibly with |
different sensors. The increasing satellite-data availability |
trend observed in the last few years (in particular, thanks to satellite constellations such as the Sentinels or the NASA |
A-Train) and the commercialization of drone-mounted |
cameras have pushed these problems to the forefront of re - |
searchers’ and analysts’ priorities. |
We have reviewed the recent models proposed in the lit - |
erature, which were grouped in four main families: 1) the approaches based on the selection of invariant features, |
2) those based on the matching of the data representation, |
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