CNN-Based Change Detection Algorithm for Wavelength-Resolution SAR Images

• This letter presents an incoherent change detectionalgorithm (CDA) for wavelength-resolution synthetic apertureradar (SAR) based on convolutional neural networks (CNNs).The proposed CDA includes a segmentation CNN, whichlocalizes potential changes, and a classification CNN, whichfurther analyzes these candidates to classify them as real changesor false alarms. Compared to state-of-the-art solutions on theCARABAS-II data set, the proposed CDA shows a significantimprovement in performance, achieving, in a particular setting,a detection probability of 99% at a false alarm rate of0.0833/km2

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

Synthetic aperture radar

Image segmentation

Clustering algorithms

Image resolution

Prediction algorithms

Performance evaluation

Training

Publication and Content Type

ref (subject category)

art (subject category)