IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 1 Interactive …sahbi/grsl2017.pdf · 2017-09-21 ·...
Transcript of IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 1 Interactive …sahbi/grsl2017.pdf · 2017-09-21 ·...
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 1
Interactive Satellite Image Change Detection
with Context-Aware Canonical Correlation
AnalysisHichem Sahbi, Member, IEEE
Abstract
Automatic change detection is one of the remote sensing applications that has received an increasing attention
during the last years. However, fully automatic solutions reach their limitation; on the one hand, it is difficult to
design general decision criteria able to select area of changes for images under various acquisition conditions, and
on the other hand, the relevance of changes may differ from one user to another.
In this paper, we introduce an alternative change detection method based on relevance feedback. The proposed
algorithm is iterative and based on a query and answer (Q&A) model that (i) asks the user questions about the
relevance of his targeted changes, and (ii) according to these answers updates change detection results. Our method
is also based on a new formulation of canonical correlation analysis (CCA), referred to as context-aware CCA, that
learns transformations which map data from different input spaces (related to multi-temporal satellite images) into
a common latent space which is sensitive to relevant changes while being resilient to irrelevant ones. These CCA
transformations correspond to the optimum of a particular constrained maximization problem that mixes an alignment
term with a context-based regularization criterion. The particularity of this novel CCA approach, resides in its ability
to exploit spatial geometric context resulting into better performances compared to other CCA approaches, as shown
in experiments.
Index Terms
Satellite image change detection, canonical correlation analysis, resilience, context, relevance feedback
I. INTRODUCTION
Change detection is the process of identifying occurrences of targeted changes into a scene at a given instant t1
w.r.t the same scene, acquired at t0 < t1. In remote sensing satellite imagery, acquisitions may be of different natures
and applications are numerous ranging from studying environmental variations (melting glacier, deforestation,
etc.) [1], [2], [3], to assessing damaged areas after catastrophe (flooding, earth-quakes, fires, tornados, etc.) [4], [5],
[6], to video surveillance and mapping services. Early change detection algorithms were initially based on simple
comparisons of multi-temporal signals, via image differences and thresholding, using vegetation indices, principal
component and change vector analyses, operating either at the pixel or the object levels (see [7], [8], [9], [10], [11],
Manuscript received June 23, 2016; revised October 5, 2016; accepted December 26, 2016.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2
[12], [13] and references therein).
Depending on use-cases, one may identify relevant changes (appearance or disappearance of entities/objects into
scenes either continuously or discontinuously in space/time) and many irrelevant changes (due to radiometric and
atmospheric variations, shadow, registration errors, insignificant local motions of objects like waving trees, etc.); for
instance, in damage assessment after natural catastrophes, relevant changes – for some users/operators – might be
areas of building destructions and flooded zones, while for other users relevant changes might be obstacles in roads.
Existing change detection methods (see for instance [14], [15], [16], [17], [18]) rely on a preliminary preprocessing
step that attenuates the effects of irrelevant changes, by finding parameters of sensors for registration as well as
correcting radiometric effects, occlusions and shadows [19], [20]. Other methods [21], [22], [23], [24], [25], [26],
[27] either ignore irrelevant changes or consider them as a part of appearance model design and are able to detect
relevant changes while being resilient to irrelevant ones.
Among automatic change detection solutions, correlation-based methods (such as CCA) are particularly successful
(see for instance [28], [29], [30]), but their success is highly dependent on the following hypothesis: information
(pixels, patches, etc.) from the same physical areas between multi-temporal images are highly correlated when
irrelevant changes occur, and uncorrelated with relevant changes. However, this hypothesis does not stand all the
time in practice; on the one hand, change detection methods operating at the pixel-level (and even at the patch-
level), are powerless to capture sufficiently discriminating information in order to precisely decide whether a pixel
or a patch belongs to a “change” or “no-change” class. Besides, pixel and patch-based methods require accurate
alignments through multi-temporal images which may be difficult to obtain as pixel and patch intensities are very
sensitive to irrelevant changes including noise and radiometric variations. On the other hand, the frontier between
relevant and irrelevant changes is sometimes narrow and may differ from one user to another, and hence requires
the intervention of users to designate a few examples of positive and negative changes according to their intentions,
in order to obtain accurate change decision criteria.
In this paper, we tackle these issues and we propose a change detection algorithm, for satellite images, based
on relevance feedback (RF), a.k.a interactive image search, and a novel CCA method. The latter, referred to as
context-aware CCA, models the statistical correlation between satellite image data (taken at different instants) and
robustly map image regions (belonging to the same physical locations) from input images to latent spaces while
being sensitive to relevant changes and resilient to irrelevant ones. Our contribution considers the spatial context as
a part of CCA design and this produces significantly better performances, especially when the spatial coherence of
changes is relevant1. The formulation is based on the optimization of an objective function mixing two terms; the
first one partly similar to standard CCA seeks to maximize the correlation between aligned “no-changes” in satellite
image data (taken at different instants and under different acquisition conditions) while at the same time it reduces
the correlation between “changes”. The second term is a regularization criterion that diffuses the correlations,
through context, between neighboring data, resulting into an extra gain as shown through experiments in change
1when changes are due to natural hazards such as tornados, even though their first impact points are difficult to predict precisely, tornados
usually follow smooth paths so changes usually spread smoothly in areas impacted by these phenomena.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 3
detection.
II. OUR RF-BASED CHANGE DETECTION AT A GLANCE
Define I0 = {u1, . . . ,un}, I1 = {v1, . . . ,vn} as two satellite image patches (referred to as reference and test
images resp.) captured at instants t0 and t1 (t0 < t1). Consider I = {x1, . . . ,xn} as a set of registered patch
pairs, with xi = (ui,vi) ∈ R2d and Y = {y1, . . . ,yn} the underlying unknown labels. Our goal is to design
an RF-based change detection algorithm which predicts the unknown labels {yi}i with yi = +1 if the test patch
vi ∈ I1 corresponds to a “change” w.r.t its reference patch ui ∈ I0; and yi = −1 otherwise. Let Dt ⊂ I be a
display (subset of patch pairs with |Dt| = 16� |I| in practice) shown to an oracle (user) at iteration t and let Ytbe the unknown labels of Dt. Starting from a random D0, including representative samples in I, we update our
change detection criteria by running the following steps for t = 0, . . . , T − 1 (In practice, T = 10); see Fig. 1
Step 1 (Oracle model). Label display Dt with a known-only-by-the-user oracle function (denoted C(.)) and assign
C(Dt) to Yt. As our change detection ground-truth is objective, we assume deterministic oracle functions only.
Step 2 (Learning model). Train a classifier ft(.) on data labeled, so far, ∪tk=0(Dk,Yk) to predict unknown labels
in I − ∪tk=0Dk. In practice, we use LIBSVM with the triangular kernel [31], in order to build ft(.); this choice
was motivated by the good performance of RF when using the triangular kernel compared to many other kernels
(see [31], [32]). Note that SVMs are trained on top of novel context-aware CCA features, and this is the main
contribution of this work.
Step 3 (Display model). Select the next display Dt+1 ⊂ I − ∪tk=0Dk to label by the oracle using two strategies,
closely related to active learning [33]: exploration and exploitation. The former selects data to discover new modes of
our change detection criteria {ft}t while the latter locally refines these criteria. Our strategy seeks a balance between
exploration and exploitation: at t = 0, we apply exploration, then at each iteration t ≥ 1, we select the subsequent
display Dt+1 depending on how good was the previous one; i.e., we either keep the previous action (exploration
or exploitation) or we switch from one to another, depending on a score St =∑
x∈Dt1{sign[ft−1(x)] 6=C(x)} which
measures how informative is display Dt obtained using the previous action2.
III. ENHANCING THE LEARNING MODEL WITH CCA
Standard CCA (e.g., [34]) finds two transformations that map aligned data {(ui,vi)}i in I0 × I1 into a latent
space while maximizing their correlation. Let Pu, Pv denote these transformations which respectively correspond to
reference and test images. CCA finds these transformations by maximizing P′vCvuPu, subject to P′uCuuPu = 1,
P′vCvvPv = 1; here ′ stands for the transpose operator, Cvu (resp. Cuu, Cvv) correspond to the interclass
(resp. intraclass) covariance matrices of data in I0, I1, and equality constraints control the effect of scaling on the
solution. One can show that problem above is equivalent to solving the generalized eigenproblem CuvC−1vv CvuPu =
γ2CuuPu with Pv =1γ C−1vv CvuPu. In practice, learning these two transformations requires labeled “no-changes”
in I. A variant in [30], referred to as discriminant CCA, combines both “no-changes” and “changes” discriminatively
2a good action should produce a display to correct as many change detection results as possible thereby better refining the subsequent decision
criterion.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 4
Satellite Image
Data
Oracle
Learning
CCA+SVM
New
Display (Q
uestions)
Initial Display
Display
ModelCCA features + Classifier
Fee
dbac
k (A
nsw
ers/
Lab
els)
Fig. 1. Flowchart of our iterative and interactive change detection algorithm.
Reference patches Test patches
latent space (CA CCA)
Pu
φv(vi)
φu(ui)
Pv
viui
Reference patches Test patches
latent space (CA CCA)
Pu Pv
ui vi
φu(ui)
φv(vi)
Fig. 2. This figure shows the diffusion of correlations between neighboring patch pairs when using context-aware CCA; the thickness of the
dashed blue lines corresponds to the amount of patch correlations between reference and test images. (Left) Though patch pairs (ui,vi) are
initially weakly correlated, they become highly correlated in the latent space as their contextual patches (i.e., immediate neighbors) are highly
correlated. (Right) In contrast, though patch pairs (ui,vi) are initially highly correlated, they become weakly correlated in the latent space as
their contextual patches are weakly correlated.
and provides better results compared to standard CCA (see also Section IV); precisely, discriminant CCA seeks
to maximize P′vC−vuPu − λP′vC
+vuPu with λ ∈ R+ and C−vu (resp.C+
vu) being the covariance matrix of negative
(resp. positive) data in {(ui,vi)}i. However, using only labeled patches is not enough and further gain is obtained
when considering the context of these patches as shown subsequently.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 5
A. Context-Aware CCA
For each patch ui, we define an anisotropic (typed) neighborhood system {Nc(i)}8c=1 which corresponds to the
eight spatial neighbors of ui in a regular grid; for instance when c = 1, N1(i) corresponds to the top-left neighbor
of ui (see Fig. 2). Using {Nc(.)}8c=1, we consider for each c an intrinsic adjacency matrix Wcu whose (i, k)th entry
is set as Wcu,i,k ∝ 1{k∈Nc(i)}; here 1{} is the indicator function equal to 1 iff i) the patch uk is neighbor to ui
and ii) its relative position is typed as c (c = 1 for top-left, c = 2 for left, etc. following an anticlockwise rotation),
and 0 otherwise. Similarly, we define the matrices {Wcv}c for patches {vi}i.
We introduce our main contribution; a novel context-aware approach for CCA. Considering a small subset
{(ui,vi)}i ⊂ I with known labels {yi}i, and now Pu, Pv as transformation matrices, we propose to find the
latter as
maxPu,Pv
tr(U′PuP′vVD) + β
8∑c=1
tr(U′PuP
′vVWc
vV′PvP
′uUWc′
u
)s.t. P′
uCuuPu = Id and P′vCvvPv = Id.
(1)
The non-matrix forms of these two terms are given subsequently. In this constrained maximization problem, β ≥ 0,
tr is the trace, U, V are two matrices of aligned patch pairs {(ui,vi)}i, Id is the d× d identity matrix and D is a
diagonal matrix with its given entry Dii set proportional to (a) −1 if yi = +1, (b) +1 if yi = −1 and (c) 0 if yi
is unknown. This particular setting of D makes CCA discriminant; indeed, the left-hand side term of this objective
function (equivalent to∑i,j〈P′uui,P′vvj〉Dij) aims to maximize the correlation between patch pairs with negative
labels (i.e., “no-changes”), while at the same time, it minimizes the correlation between patch pairs with positive
labels (i.e., relevant “changes”). Hence, this left-hand side term is strictly equivalent to discriminant CCA. With this
discriminative setting, the learned transformations Pu, Pv generate latent data representations φu(ui) = P′uψf (ui),
φv(vi) = P′vψf (vi), which are robust against irrelevant changes3 (i.e., ‖φu(ui) − φv(vi)‖2 0 for yi = −1)
while also being sensitive to relevant changes (i.e., ‖φu(ui)− φv(vi)‖2 is large for yi = +1). This results into a
better discrimination between “changes” and “no-changes”.
Using the above definition of {Wcu}c, {Wc
v}c, the right-hand side term of the objective function (1) is equivalent
to
β∑c
∑i,j〈P′uui,P′vvj〉
∑k,`〈P′uuk,P′vv`〉Wc
u,i,kWcv,j,`; the latter corresponds to a neighborhood (or context)
criterion which considers that a high value of the correlation 〈P′uui,P′vvj〉, in the learned latent space, should
imply high correlation values in the neighborhoods {Nc(i)×Nc(j)}c (see Fig. 2). Hence, this term enhances the
robustness of the correlation between patch pairs in the learned latent space. Put differently, if a given patch pair
(ui,vj) is surrounded by highly correlated pairs, then the correlation between (ui,vj) should be maximized and
vice-versa.
3with ψf (ui) being a feature vector associated to ui, see experiments.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 6
B. Optimization
Considering Lagrange multipliers for the equality constraints in Eq. (1), one may show that optimality conditions
(related to the gradient of Eq. (1) w.r.t Pu, Pv and the Lagrange multipliers) lead to the generalized eigenproblem
K′vuC−1vv KvuPu = CuuPuΛ
2
with Pv = C−1vv KvuPuΛ−1,
(2)
here Kvu = VDU′ +β∑c VWc
vV′PvP
′uUWc′
u U′
+β∑c VWc′
v V′PvP′uUWc
uU′,
(3)
and Λ2 is the hadamard power of the diagonal matrix Λ. As K′vuC−1vv Kvu is Hermitian and Cuu is positive
semi-definite, the eigenproblem in Eq. (2) admits real eigenvalues (in Λ2) and its eigenvectors, when multiplied by
C12uu (resp. C
12vv), are mutually orthogonal and from an orthogonal basis; here C
12uu, C
12vv are square roots of Cuu,
Cvv respectively.
We solve the above eigenproblem iteratively. For each iteration τ , we fix P(τ)u , P
(τ)v (in Kvu) and we find the
subsequent P(τ+1)u , P
(τ+1)v by solving the eigenproblem in Eq. (2); initially, P
(0)u , P
(0)v are set using projection
matrices of standard CCA and this turns out to provide better behavior compared to random initializations (i.e., it
rapidly converges to a unique solution of objective function 1). In practice, convergence to a fixed point is observed
in less than five iterations4.
IV. EXPERIMENTAL VALIDATION
We evaluate the performances of our interactive change detection algorithm using a dataset of 4, 400 non
overlapping patch pairs (of 30×30 pixels in RGB) taken from two registered (reference and test) GeoEye-1 satellite
images of 2, 400 × 1, 652 pixels with a spatial resolution of 1.65m/pixel. These images correspond to the same
area of Jefferson (Alabama) taken respectively in 2010 and in 2011 with many changes due to tornados (building
destruction, etc.) and no-changes (including irrelevant ones as clouds). The underlying ground truth contains 4, 275
negative patch pairs (“no-changes” and irrelevant ones) and only 125 positive patch pairs (relevant changes), so
< 3% of these patches correspond to relevant changes (see Fig. 3a–c). Note that the spatial distribution of changes
in this set is globally smooth, so this dataset is suitable to evaluate the impact of our proposed context-aware CCA
method (see Sections IV-A, IV-B).
Each patch (in reference and test images) is encoded with d = 200 coefficients corresponding to its projection on
the d principal axes of PCA. These principal axes of PCA were estimated using all patches of the reference image
and capture more than 95% of the statistical variance of the data. Afterwards, each patch pair xi = (ui,vi) in I is
described either without CCA as i) ψf (vi) − ψf (ui) with ψf (ui) being the projection of ui using PCA or as ii)
φv(ψf (vi))−φu(ψf (ui)) when the CCA latent representations φu(.), φv(.) are considered; since the learned CCA
transformations are linear, the dimensionality of φu(.), φv(.) is bounded by d and in practice, we keep all the d
dimensions in order to avoid any substantial loss of information w.r.t the original patches. Performances are reported
4The complexity of solving Eq. (2) depends mainly on the dimension d (d� n).
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 7
(a) (b) (c)
Fig. 3. (a–c) Area taken from the reference image (before tornados) and test image (after tornados) as well as their ground truth in “Jefferson”;
“red” stands for relevant changes while “white” stands for irrelevant changes (mainly clouds), the remaining locations correspond to no-changes.
using equal error rate (EER) on unlabeled data of I. EER is the balanced generalization error that equally weights
errors in “change”, “no-change” classes and it is defined as 12 (
# of incorrect change detections# of change detections + # of undetected actual changes
# of actual changes )×100.
Smaller EER implies better performance.
A. Impact of Relevance Feedback
We compare our RF-based change detection criteria {ft(.)}Tt=0, against four RF-free criteria; i.e., independent
of t
(i) Image difference: a pair xi = (ui,vi) ∈ I is declared as a change iff ‖ψf (ui)− ψf (vi)‖22 0.
(ii) Large scale (monolithic) SVM: we train an SVM decision function (denoted f ) and we use it to detect changes
in I. The training set of f (denoted T ′ = {(x′i,y′i)}i) is different from I; it includes 220 positive examples and
4, 180 negative examples extracted from other GeoEye-1 satellite images (including 2, 400 × 1, 652 pixels) of the
nearby area of Tuscaloosa (Alabama) taken respectively in 2010 and in 2011 with many changes due to tornados
that also happen in 2011. Note that the set T ′ is much larger than the one used to train the final classifier fT−1
(which has only∑T−1t=0 |Dt| = 160 examples at the end of our relevance feedback process).
(iii) Large scale (monolithic) CA-DCCA5: this setting is similar to monolithic SVM, but now SVM is trained on
top of CA-DCCA features instead of PCA.
(iv) Large scale (monolithic) FDA6: this setting is also similar to monolithic SVM with the only difference being
the used criterion; FDA is used as a classifier instead of SVM.
Fig. 4a shows EERs of these baselines (i)–(iv) as well as our proposed RF method (with and without CA-DCCA);
we first observe that monolithic CA-DCCA achieves the best performances among all the baselines, and this clearly
shows that the CA-DCCA learned on the area of “Tuscaloosa” is also (relatively) suitable for “Jefferson”. Whereas
these baselines and our proposed RF method (for t ≤ 1) have high EERs, our RF method rapidly reduces the
EER and overtakes all the monolithic criteria, at the end of the iterative process. This comes essentially from the
5CA-DCCA stands for context-aware discriminant CCA transformations learned here using patch pairs belonging to the area of “Tuscaloosa”.
Once trained, all these transformations and SVM are applied to patches of “Jefferson”.6Fisher Discriminant Analysis.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 8
1 3 5 7 9
10
15
20
25
30
35
Iteration number (t)
Equal E
rror
Rate
(%
)
Image Difference
Large Scale (Monolithic) FDA
Large Scale (Monolithic) SVM
Large Scale (Monolithic) CA−DCCA
Proposed RF with no CCA
Proposed RF with CA−DCCA
1 3 5 7 910
15
25
35
45
Iteration number (t)
Equal E
rror
Rate
(%
)
no CCAStandard CCADiscriminant CCAContext−Aware Standard CCAContext−Aware Discriminant CCAFDA
−8 −6 −4 −2 0 211
12
13
14
15
16
17
18
19
log2(β)
Equal E
rror
Rate
(%
)
Context−Aware Discriminant CCA
(a) (b) (c)
Fig. 4. (a) Comparison of our RF method against several baselines: image difference and large scale (monolithic) criteria; in this figure, noCCA
means that the underlying SVMs are learned on top of PCA features. (b) Evolution of EER results w.r.t the iteration number (t), for SVMs
built on top of different CCA versions (for context-aware CCAs, β is set to 0.15) and also using FDA; again noCCA means that SVMs are
learned on top of PCA features. (c) This figure shows the evolution of EER (of the RF-based SVMs built on top of context-aware discriminant
CCA) w.r.t β; these values correspond to EER performances at the end of the RF process (i.e., when t = 9). Note that β → 0 is equivalent to
discriminant CCA. All these results are obtained by averaging EERs of 10 RF runs.
adaptation of decision functions {ft(.)}t to the user’s intention as well as to reference and test images in I. This
is also due to the positive impact of CA-DCCA on RF as shown in Fig. 4 and also subsequently.
B. Impact of Different CCA
Now we study the impact of different CCA methods on the RF performances. Fig. 4b shows the RF performances
without CCA and with (standard, discriminant and context-aware) CCA w.r.t the iteration number t; in these
experiments, we consider the context-aware version of both standard and discriminant CCA. We also compare the
performance of all these CCA versions against FDA; the setting of the latter is similar to all the CCA versions
with the only difference being the use of FDA instead of CCA+SVM (as also discussed in Section IV-A). Note
that all these results were obtained by averaging EERs of 10 RF runs, each one corresponds to a random setting
of display D0 (see again Section II). These EERs decrease as t increases and reach their smallest values at the
end of the iterative process, i.e., when decision criteria {ft(.)}t are well trained/adapted to the reference and test
satellite images, and this happens after 10 iterations only. Fig. 4c shows the EERs of our RF algorithm (after
10 iterations) built upon context-aware discriminant CCA; these EERs globally decrease as β increases/reaches
intermediate values and EERs increase again for larger values of β. From all these observations, it is clear that the
proposed context-aware CCA combined with RF has a clear gain compared to the other settings and it is able to
find relevant changes and discard many irrelevant ones (in few iterations).
Finally, CCA and SVM learning/classification (as well as the display selection strategy discussed in section II) run
promptly (in less than 1s at each iteration t on a Quad Core 3.60GHz PC), provided that PCA features are extracted
off-line; so change detection results are iteratively updated in real-time.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 9
V. CONCLUSION
We introduced in this paper a novel change detection algorithm based on relevance feedback and a new variant of
CCA referred to as context-aware CCA. Our method considers both labeled and unlabeled data when learning the
CCA transformations. This is achieved by optimizing an objective function mixing two terms: the first one relies on
a discriminative setting that maximizes (resp. minimizes) correlations between “no changes” (resp. “changes”). The
second term acts as a regularizer that makes correlations spatially smooth and provides us with robust context-aware
latent representations. As shown through experiments, the relative gain of our method is substantial especially when
changes are smoothly spread; this is valuable for changes impacted by natural hazards following regular paths such
as tornados.
REFERENCES
[1] K. Rokni, A. Ahmad, K. Solaimani, and S. Hazini, “A new approach for surface water change detection: integration of pixel level image
fusion and image classification techniques,” International Journal of Applied Earth Observation and Geoinformation, vol. 34, pp. 226–234,
2015.
[2] S. Jamali, P. Jonsson, L. Eklundh, J. Ardo, and J. Seaquist, “Detecting changes in vegetation trends using time series segmentation,”
Remote Sensing of Environment, vol. 156, pp. 182–195, 2015.
[3] Z. Zhu and C. E. Woodcock, “Continuous change detection and classification of land cover using all available landsat data,” Remote
sensing of Environment, vol. 144, pp. 152–171, 2014.
[4] D. Brunner, G. Lemoine, and L. Bruzzone, “Earthquake damage assessment of buildings using vhr optical and sar imagery,” IEEE Trans.
Geosc. Remote Sens., vol. 48, no. 5, pp. 2403–2420, 2010.
[5] H. Gokon, J. Post, E. Stein, S. Martinis, A. Twele, M. Muck, C. Geiss, S. Koshimura, and M. Matsuoka, “A method for detecting buildings
destroyed by the 2011 tohoku earthquake and tsunami using multitemporal terrasar-x data,” GRSL, vol. 12, no. 6, pp. 1277–1281, 2015.
[6] N. Longbotham, F. Pacifici, T. Glenn, A. Zare, M. Volpi, D. Tuia, E. Christophe, J. Michel, J. Inglada, J. Chanussot et al., “Multi-modal
change detection, application to the detection of flooded areas: Outcome of the 2009–2010 data fusion contest,” Selected Topics in Applied
Earth Observations and Remote Sensing, vol. 5, no. 1, pp. 331–342, 2012.
[7] J. Deng, K. Wang, Y. Deng, and G. Qi, “Pca-based land-use change detection and analysis using multitemporal and multisensor satellite
data,” IJRS, vol. 29, no. 16, pp. 4823–4838, 2008.
[8] E. F. Lambin and A. H. Strahlers, “Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change
processes using high temporal-resolution satellite data,” Remote Sensing of Environment, vol. 48, no. 2, pp. 231–244, 1994.
[9] R. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: A systematic survey,” IEEE Trans. on Im Proc,
vol. 14, no. 3, pp. 294–307, 2005.
[10] G. Moser and S. Serpico, “Generalized minimum error thresholding for unsupervised change detection from amplitude sar imagery,” IEEE
TGRS, vol. 44, no. 10, pp. 2972–2983, 2006.
[11] S. Liu, L. Bruzzone, F. Bovolo, M. Zanetti, and P. Du, “Sequential spectral change vector analysis for iteratively discovering and detecting
multiple changes in hyperspectral images,” TGRS, vol. 53, no. 8, pp. 4363–4378, 2015.
[12] M. Hussain, D. Chen, A. Cheng, H. Wei, and D. Stanley, “Change detection from remotely sensed images: From pixel-based to object-based
approaches,” JPRS, vol. 80, pp. 91–106, 2013.
[13] G. Chen, G. J. Hay, L. M. Carvalho, and M. A. Wulder, “Object-based change detection,” IJRS, vol. 33, no. 14, pp. 4434–4457, 2012.
[14] J. Zhu, Q. Guo, D. Li, and T. C. Harmon, “Reducing mis-registration and shadow effects on change detection in wetlands,” Photogrammetric
Engineering & Remote Sensing, vol. 77, no. 4, pp. 325–334, 2011.
[15] A. Fournier, P. Weiss, L. Blanc-Fraud, and G. Aubert, “A contrast equalization procedure for change detection algorithms: applications to
remotely sensed images of urban areas,” In ICPR, 2008.
[16] N. Bourdis, D. Marraud, and H. Sahbi, “Camera pose estimation using visual servoing for aerial video change detection,” in IEEE IGARSS,
2012, pp. 3459–3462.
[17] Carlotto, “Detecting change in images with parallax,” In Society of Photo-Optical Instrumentation Engineers, 2007.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 10
[18] N. Bourdis, D. Marraud, and H. Sahbi, “Constrained optical flow for aerial image change detection,” in IEEE IGARSS, 2011, pp. 4176–4179.
[19] X. Chen, L. Vierling, and D. Deering, “A simple and effective radiometric correction method to improve landscape change detection across
sensors and across time,” RSE, vol. 98, no. 1, pp. 63–79, 2005.
[20] S. Leprince, S. Barbot, F. Ayoub, and J.-P. Avouac, “Automatic and precise orthorectification, coregistration, and subpixel correlation of
satellite images, application to ground deformation measurements,” TGRS, vol. 45, no. 6, pp. 1529–1558, 2007.
[21] Pollard, “Comprehensive 3d change detection using volumetric appearance modeling,” Phd, Brown University, 2009.
[22] R. Wiemker, “An iterative spectral-spatial bayesian labeling approach for unsupervised robust change detection on remotely sensed
multispectral imagery,” In Proc. CAIP, LNCS 1296, pp. 263–270, 1997.
[23] M. Molinier, J. Laaksonen, and T. Hame, “Detecting man-made struct and changes in sat imagery with a content-based information retrieval
system built on self-organizing maps,” TGRS, vol. 45, no. 4, 2007.
[24] O. Sjahputera, G. Scott, B. Claywell, M. Klaric, N. Hudson, J. Keller, and C. Davis, “Clustering of detected changes in high-resolution
satellite imagery using a stabilized competitive agglomeration algorithm,” IEEE TGRS, vol. 49, no. 12, 2011.
[25] A. A. Nielsen, “The regularized iteratively reweighted mad method for change detection in multi-and hyperspectral data,” IEEE Transactions
on Image processing, vol. 16, no. 2, pp. 463–478, 2007.
[26] C. Wu, B. Du, and L. Zhang, “Slow feature analysis for change detection in multispectral imagery,” TGRS, vol. 52, no. 5, pp. 2858–2874,
2014.
[27] N. Bourdis, D. Marraud, and H. Sahbi, “Spatio-temporal interaction for aerial video change detection,” in IGARSS, 2012, pp. 2253–2256.
[28] J. Im, J. Jensen, and J. Tullis, “Object-based change detection using correlation image analysis and image segmentation,” International
Journal of Remote Sensing, vol. 29, no. 2, pp. 399–423, 2008.
[29] C. Tarantino, M. Adamo, R. Lucas, and P. Blonda, “Detection of changes in semi-natural grasslands by cross correlation analysis with
worldview-2 images and new landsat 8 data,” RSE, vol. 175, pp. 65–72, 2016.
[30] H. Sahbi, “Discriminant canonial correlation analysis for interactive satellite image change detection,” IEEE IGARSS, 2015.
[31] F. Fleuret and H. Sahbi, “Scale invariance of support vector machines based on the triangular kernel,” SCTV (part of ICCV), 2003.
[32] M. Ferecatu, “Image retrieval with active relevance feedback using both visual and keyword-based descriptors,” PhD thesis, 2005.
[33] H. Sahbi, “Relevance feedback for satellite image change detection,” in IEEE ICASSP, 2013, pp. 1503–1507.
[34] D. Hardoon, S. Szedmak, and J. Shawe-Taylor, “Canonical correlation analysis: An overview with application to learning methods,” Neural
computation, vol. 16, no. 12, pp. 2639–2664, 2004.