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The performance of synthetic aperture radar (SAR) image classification based on a conventional convolutional neural network (CNN) is limited by a trade-off between immunity to speckle noise and the ability to locate boundaries accurately. Difficulties regarding the accurate location of boundaries are a result of the smoothing effect of the pooling layer. To address this issue, we propose a novel framework called SRAD-CNN for SAR image classification. In this framework, we apply a filtering layer constructed according to prior knowledge of the speckle reducing anisotropic diffusion (SRAD) filter. The filtering layer can not only reduce speckle but also enhance the boundaries. The main parameter that controls the degree of filtering can be optimized adaptively by a backpropagation algorithm. Image patches adaptively filtered by the filtering layer are then put into the CNN layers to assign a label. Due to the effect of the filtering layer, for our proposed SRAD-CNN, both the speckle noise immunity and the sensitivity to boundaries are superior to those of conventional CNN.To confirm the performance of the proposed SRAD-CNN, we conducted experiments using both simulated and real SAR images. The experimental results demonstrated that the parameter of the filtering layer could be optimized adaptively for different scenes, different noise levels, and different image resolutions. The SRAD-CNN outperformed the conventional CNN in both overall classification accuracy and maintenance of boundary accuracy on images with different resolutions and noise levels with limited training samples. [ABSTRACT FROM AUTHOR]

In this article, a novel pointwise approach is proposed for change detection in bi-temporal synthetic aperture radar (SAR) images using stereograph model. Due to the fact that SAR image suffers from the speckle noise, a pointwise approach based on a set of characteristic points only, not on the whole pixels, seems to be more efficient. Moreover, the correlations of neighbourhood points which have different locations in bi-temporal SAR images should be studied to repress the speckle in change detection. Therefore, the stereograph model, which extends the graph model to three-dimensional space, is designed to connect the local maximum pixels on bi-temporal SAR images and can be used to capture the multiple-span neighbourhood information from the edges. Furthermore, a specialized change measure function is presented to quantify the neighbourhood information from stereograph model, and thus, a novel nondense difference image (NDI) is generated. Finally, a traditional classification method is used to analyse the NDI into changed class and unchanged class. Experiments on real SAR images show that the proposed NDI can improve separability between changed and unchanged areas, and the final results possess high accuracy and strong noise immunity for change detection tasks with noise-contaminated SAR images. [ABSTRACT FROM AUTHOR]