2 research outputs found
Canonical Correlation Analysis for Misaligned Satellite Image Change Detection
Canonical correlation analysis (CCA) is a statistical learning method that
seeks to build view-independent latent representations from multi-view data.
This method has been successfully applied to several pattern analysis tasks
such as image-to-text mapping and view-invariant object/action recognition.
However, this success is highly dependent on the quality of data pairing (i.e.,
alignments) and mispairing adversely affects the generalization ability of the
learned CCA representations. In this paper, we address the issue of alignment
errors using a new variant of canonical correlation analysis referred to as
alignment-agnostic (AA) CCA. Starting from erroneously paired data taken from
different views, this CCA finds transformation matrices by optimizing a
constrained maximization problem that mixes a data correlation term with
context regularization; the particular design of these two terms mitigates the
effect of alignment errors when learning the CCA transformations. Experiments
conducted on multi-view tasks, including multi-temporal satellite image change
detection, show that our AA CCA method is highly effective and resilient to
mispairing errors
Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion
Classifying multi-temporal scene land-use categories and detecting their
semantic scene-level changes for imagery covering urban regions could
straightly reflect the land-use transitions. Existing methods for scene change
detection rarely focus on the temporal correlation of bi-temporal features, and
are mainly evaluated on small scale scene change detection datasets. In this
work, we proposed a CorrFusion module that fuses the highly correlated
components in bi-temporal feature embeddings. We firstly extracts the deep
representations of the bi-temporal inputs with deep convolutional networks.
Then the extracted features will be projected into a lower dimension space to
computed the instance-level correlation. The cross-temporal fusion will be
performed based on the computed correlation in CorrFusion module. The final
scene classification are obtained with softmax activation layers. In the
objective function, we introduced a new formulation for calculating the
temporal correlation. The detailed derivation of backpropagation gradients for
the proposed module is also given in this paper. Besides, we presented a much
larger scale scene change detection dataset and conducted experiments on this
dataset. The experimental results demonstrated that our proposed CorrFusion
module could remarkably improve the multi-temporal scene classification and
scene change detection results.Comment: submitte