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decision tree update and randomization are used for DA in [57]. A set of decision trees is made robust in the face of |
data set shift either by training it with the EM using den - |
sity functions from the target domain (similar to [ 54]) or |
by randomizing the decision trees (but, in this case, with - |
out control of the adaptation objective). The authors also propose a semisupervised extension where the classifiers performing poorly on the few labeled samples in the target |
domain are downweighted in the final decision. Finally, the |
DA technique proposed in [ 20] for multitemporal images |
addresses the challenging situation where the source and |
target domains have a different set of classes. The sets of |
classes of the target and the source domains are automati - |
cally identified in the DA step by the joint use of unsuper - |
vised change detection and the Jeffries–Matusita statistical distance measure. This process results in the detection of classes that appeared or disappeared between the domains. |
The semisupervised problem has also been extensively |
studied in the framework of kernel methods with SVM classifiers, which has been done especially for addressing |
sample selection bias problems (see the “Transfer Learn - |
ing and Domain Adaption” section). Most of the semisu - |
pervised techniques proposed with SVM exploit the clus - |
ter assumption, i.e., adapt the position of the hyperplane |
estimated on the source domain to the target domain, assuming that it should be located in low-density regions −38.79 −29.16 6.09 26.76 39.50.20.30.40.50.60.70.80.9 |
Acquisition AngleKappa |
Best Case (Labels from All Images) |
Training with Image at 6.09° OnlyPCA KPCA |
GM SSMALDA−38.79 −29.16 6.09 26.76 39.50.20.30.40.50.60.70.80.9 |
Acquisition AngleKappa |
SVM |
Figu Re 7. The classification results over the five Rio de Janeiro acquisitions (adapted from [38]). There are 100 labeled samples per class |
from the nadir image used to train a classifier and then to test on the others. In the SSMA experiment, 50 labeled pixels per class are used |
from the other acquisitions. |
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june 2016 ieee Geoscience and remote sensin G ma Gazine 51 |
of the feature space. In [ 58], the authors employ the trans - |
ductive SVM, a method that iteratively moves the deci - |
sion boundary of the SVM classifier toward low-density |
areas of the unlabeled target domain. Other semisuper - |
vised approaches are imported in remote sensing in [ 59], |
where the SVM semisupervised learning is addressed in the primal formulation of the cost function. In [ 60], the |
authors regularize the SVM solution by adding a new |
term in the optimization accounting for the divergence |
between source and target domains (i.e., the MMD dis - |
cussed in the “Adapting Data Distributions” section). By |
doing so, the decision function selected depends on a ker - |
nel that both projects in a discriminative space and mini - |
mizes the shift between training and test data. In [ 61], |
the authors cast the DA problem as a multitask learning |
problem, where each source–domain pair (i.e., each task) is solved by deforming the kernel by sharing information |
among the tasks. |
The Laplacian SVM technique applied to the classifica - |
tion of multispectral remote sensing images is presented in [62], which exploits an additional regularization term on |
the geometry of both the labeled and the unlabeled sam - |
ples by using the Laplacian graph. In [ 63], the authors also |
use a manifold-regularized classifier in a semisupervised |
setting, where the adaptation is performed by adding semi - |
labeled examples from the target domain. |
A specific semisupervised SVM that is defined for ad - |
dressing DA problems is presented in [ 64]. The domain ad - |
aptation SVM (DASVM) starts from a standard supervised |
learning on the training samples of the source domain, |
which is followed by an iterative procedure. At each itera - |
tion, it includes in the learning cost function a subset of |
unlabeled samples of the target domain adequately select - |
ed while gradually removing the training samples of the |
source domain. At convergence, the DASVM can accurately |
classify the samples of the target domain.ADAPTATION OF THE CLASSIFIER |
BY ACTIVE LEARNING In most of the previously mentioned approaches, it is as - |
sumed that no label information can be obtained in the |
newly acquired target domains (i.e., semisupervised DA). |
This assumption may hinder the success of classification in |
the case of very strong deformations or when new classes that are unseen during training appear in the test data. A |
small amount of labeled data issued from the target do - |
main may solve this problem efficiently. However, since |
the acquisition is timely and can be costly, it becomes man - |
datory to choose the samples well. AL strategies have been |
proposed to tackle this challenging task and guide the DA process with the selection of the most informative target |
samples [19], [27], [28], [ 65]–[67 ]. |
AL is the name of a set of methodologies aiming at the |
interaction between a user and a prediction model, where the user provides labels based on knowledge of the task |
to be solved and the model performs the prediction and highlights samples for which it has the highest uncertainty |
[68]. By focusing on these samples, the user provides the |
labels where they help the most and, thus, allows the clas - |
sifier to migrate in a fast way toward the optimal model. Surveys on AL methods applied to remote sensing can be |
found in [ 69]–[71 ]. In the case of DA, the user provides |
examples coming from the target domain alone, and the |
optimal classifier is the one that would have been obtained |
with several examples in the target domain. The process starts with a classifier that is optimal for the source do - |
main and gradually evolves to model the data distribution in the target domain. Figure 8 summarizes the AL process |
for DA. |
One could apply classical AL strategies in transfer-learn - |
ing problems under the sample selection bias assumption with successful results, because classical AL will point out |
samples close to the current decision boundaries, and the |
00.511.522.53 |
Target Domain |
xiyiUncer tain |
ResponseProvides |
LabelUserSource Domain |
(XS, YS) |
(XT)ClassifierClassification |
Confidence |
Classification Map |
Figu Re 8. A flow chart of the AL paradigm for DA. |
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