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

Abstract

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase objectbased 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 (SLIC) 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% change detection accuracy using multi-temporal SAR imagery.

Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

“…However, given the irregular shape of the superpixels, excessive interference information was mixed into the rectangular box, which seriously influences the classification result. Reference [46] converted superpixels into 1D vectors first, and then, the vector converted back to 2D patches with a regular shape, which retained the spatial information to some extent. However, the above patch-based methods cannot learn in an end-to-end way.…”

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

“…The deep learning-based classification model has had a rapid development and application in recent years, and these classification methods have generated an excellent performance and higher classification accuracy rate than the abovementioned approaches. Zhang et al [47] proposed two-phase object-based deep learning for unsupervised SAR change detection. Gao et al [48] introduced the channel weighting-based deep cascade network for unsupervised change detection.…”

Hyperspectral change detection based on modification of UNet neural networks

“…These networks require a high degree of computational complexity. CD methods based on encoder-decoder segmentation techniques [25][26][27] were used to highlight the temporal changes in land cover. In recent years, different semantic segmentation was introduced based on CNN architectures.…”