SAR image classification using adaptive neighborhood-based convolutional neural network

Anjun Zhang,Xuezhi Yang,Lu Jia,Jiaqiu Ai,Zhangyu Dong

doi:10.1080/22797254.2019.1579616

Anjun Zhang, Xuezhi Yang + Show 3 more

Open Access

https://doi.org/10.1080/22797254.2019.1579616

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Abstract

ABSTRACT The convolutional neural network (CNN)-based pixel-wise synthetic aperture radar (SAR) data classification does not take fully use the spatial neighborhood information due to the fact that the impact of neighborhood pixels is not taken into consideration. The flaw of CNN-based classification method may lead to misclassification under some conditions. In this paper, we propose a novel adaptive neighborhood-based convolutional neural network (AN-CNN) for the single polarimetric synthetic aperture radar data classification. In the convolution layer, the neighborhood pixels are adaptively weighted based on their bilateral distance (spatial and feature distance) to the central pixel. In this way, different pixels have different impact on the classification result of the central pixel. The spatial distance-based weighting can reduce the misclassifications in the homogenous regions which are caused by speckle noise and the feature distance-based weighting is beneficial for the classification in the boundary regions. As a result, the misclassification is obviously reduced by the proposed AN-CNN which has a new cost function. Experimental results on simulated and real SAR data show that our proposed AN-CNN can notably improve the classification accuracy in both boundary regions and homogeneous regions compared with conventional CNN in different scenes especially when limited training samples are explored.

Highlights

Synthetic aperture radar (SAR) can provide a plenty of information of the land cover under all-weather and all-time conditions
We propose a novel adaptive neighborhood-based convolutional neural network (AN-CNN) for the single polarimetric synthetic aperture radar data classification
The results indicate that the AN-CNN shows better classification performance in both the homogeneous region and the boundary regions when compared with CNN

Summary

Introduction

Synthetic aperture radar (SAR) can provide a plenty of information of the land cover under all-weather and all-time conditions. It is of great importance to interpret the SAR data accurately and effectively. Topics about SAR image interpretation such as SAR image retrieval (Tang & Jiao, 2017; Tang, Jiao, & Emery, 2017), object recognition and SAR image classification has been rising recent years. The pixel-wise SAR image classification, where each pixel in the SAR image is assigned to one class, is the most fundamental problem in SAR image interpretation. The CNN consists of a stack of convolution layers and pooling layers with the fully connected layers on the top. Filters and biases in the convolution layer and the weight matrixes in fully connected layers are the parameters to be learned. The filter moves with a certain step within the input image to generate a convolved image.

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