Multiresolution Fully Convolutional Networks to detect Clouds and Snow through Optical Satellite Images

Topology, homogeneity and scale factors for object detection: application of eCognition software for urban mapping using multispectral satellite image

The research scope of this paper is to apply spatial object based image analysis (OBIA) method for processing panchromatic multispectral image covering study area of Brussels for urban mapping. The aim is to map different land cover types and more specifically, built-up areas from the very high resolution (VHR) satellite image using OBIA approach. A case study covers urban landscapes in the eastern areas of the city of Brussels, Belgium. Technically, this research was performed in eCognition raster processing software demonstrating excellent results of image segmentation and classification. Th…

基于位置感知神经网络的鲁棒递归多分辨率多光谱图像融合

Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks

针对多分辨率图像融合中云等异常值干扰及缺乏鲁棒递归学习模型的问题，提出一种基于位置感知神经网络的鲁棒递归图像融合框架，通过贝叶斯变分推理估计高分辨率图像，显著提升了抗云覆盖能力且不损失无云时的性能。

Geographical Knowledge-driven Representation Learning for Remote Sensing Images

The proliferation of remote sensing satellites has resulted in a massive amount of remote sensing images. However, due to human and material resource constraints, the vast majority of remote sensing images remain unlabeled. As a result, it cannot be applied to currently available deep learning methods. To fully utilize the remaining unlabeled images, we propose a Geographical Knowledge-driven Representation learning method for remote sensing images (GeoKR), improving network performance and reduce the demand for annotated data. The global land cover products and geographical location associate…

Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks

Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and remote areas. In recent years, avalanche detection in Sentinel-1 radar satellite imagery has been developed to improve monitoring. However, the current state-of-the-art detection algorithms, based on radar signal processing techniques, are still much less accurate than human experts. To reduce this gap, we propose a deep learning architecture for detecting aval…

基于深度学习的合成孔径雷达图像大范围雪崩制图

Large-Scale Avalanche Mapping from SAR Images with Deep Learning-based Change Detection

本文基于深度学习的双时相变化检测方法，利用Sentinel-1 SAR图像实现大范围雪崩制图，通过端到端流程在不同优化策略下取得高精度检测结果，展示了SAR基雪崩制图的可行性与改进方法。

用于卫星图像去噪的Riesz-Quincunx-UNet变分自编码器

Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising

本文提出将Riesz-Quincunx小波变换嵌入U-Net并结合变分自编码器，构建RQUNet-VAE混合方案，用于卫星图像去噪、分解与分割，实验表明其去噪效果优于现有方法。