Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks

by   Muhammad Shahzad, et al.

This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high resolution (VHR) synthetic aperture radar (SAR) images. In this context, the paper has two major contributions: Firstly, it presents a novel and generic workflow that initially classifies the spaceborne TomoSAR point clouds - generated by processing VHR SAR image stacks using advanced interferometric techniques known as SAR tomography (TomoSAR) - into buildings and non-buildings with the aid of auxiliary information (i.e., either using openly available 2-D building footprints or adopting an optical image classification scheme) and later back project the extracted building points onto the SAR imaging coordinates to produce automatic large-scale benchmark labelled (buildings/non-buildings) SAR datasets. Secondly, these labelled datasets (i.e., building masks) have been utilized to construct and train the state-of-the-art deep Fully Convolution Neural Networks with an additional Conditional Random Field represented as a Recurrent Neural Network to detect building regions in a single VHR SAR image. Such a cascaded formation has been successfully employed in computer vision and remote sensing fields for optical image classification but, to our knowledge, has not been applied to SAR images. The results of the building detection are illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering approximately 39 km ^2 - almost the whole city of Berlin - with mean pixel accuracies of around 93.84


CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images

Object retrieval and reconstruction from very high resolution (VHR) synt...

Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networks

Despite the advantages of all-weather and all-day high-resolution imagin...

Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN

In this letter, we propose a pseudo-siamese convolutional neural network...

Multi-Scale Spatially-Asymmetric Recalibration for Image Classification

Convolution is spatially-symmetric, i.e., the visual features are indepe...

Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data

Very High Spatial Resolution (VHSR) large-scale SAR image databases are ...

Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks

Knowledge about frequency and location of snow avalanche activity is ess...

Accurate Building Detection in VHR Remote Sensing Images using Geometric Saliency

This paper aims to address the problem of detecting buildings from remot...

Please sign up or login with your details

Forgot password? Click here to reset