Two-Phase Object-Based Deep Learning for Multi-temporal SAR Image Change Detection

by   Xinzheng Zhang, et al.

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a much negative effect on change detection. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighboring pixels into segmented into superpixel objects (from pixels) such as to exploit local spatial context. Two phases are designed in the methodology: 1) Generate objects based on the simple linear iterative clustering algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. 2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71


page 5

page 7

page 12

page 14

page 15

page 16

page 17

page 20


A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet

In this research, a novel robust change detection approach is presented ...

Deep learning networks for selection of persistent scatterer pixels in multi-temporal SAR interferometric processing

In multi-temporal SAR interferometry (MT-InSAR), persistent scatterer (P...

Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning

Small area change detection from synthetic aperture radar (SAR) is a hig...

Recognition of Changes in SAR Images Based on Gauss-Log Ratio and MRFFCM

A modified version of MRFFCM (Markov Random Field Fuzzy C means) based S...

A New Unsupervised Change Detection Approach Based on PCA Based Blocking and GMM Clustering for Detecting Flood Damage

In this paper, a new and effective unsupervised change detection techniq...

Imbalanced Learning-based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net

Change detection is a quite challenging task due to the imbalance betwee...

Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

This paper presents five different statistical methods for ground scene ...