A Reliable Mixed-Norm-Based Multiresolution Change Detector in Heterogeneous Remote Sensing Images

Analysis of heterogeneous remote sensing image is a challenging and complex problem due to the fact that the local statistics of the data to be processed can be radically different. In this article, we present a novel and reliable unsupervised change detection (CD) method to analyze heterogeneous remotely sensed image pairs. The proposed method is based on an imaging modality-invariant operator that detects at different scale levels the differences in terms of high-frequency pattern of each structural region existing in the two heterogeneous satellite images. First, this new detector is based upon a dual-norm formulation that makes our underlying CD estimation particularly robust in terms of a sensitivity/specificity tradeoff. Second, the detection process, embedded in a multiresolution framework, allows us to estimate a robust similarity or difference map that is then filtered out by a superpixel-based spatially adaptive filter to further increase its reliability against noise. Finally, changes are then identified from this similarity map by a simple binary clustering process that also takes into account the spatial contextual information around each pixel. Experimental results involving different types of heterogeneous remotely sensed image pairs confirm the robustness of the proposed approach.

• Publication year

  2019

• Venue

  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

• Publication date

  2019-08-28

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.1109/JSTARS.2019.2934602
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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