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Found 7 papers, 2 papers with code
Towards Global Glacier Mapping with Deep Learning and Open Earth Observation Data
Accurate global glacier mapping is critical for understanding climate change impacts.
DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation
Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery.
Multiresolution Fully Convolutional Networks to detect Clouds and Snow through Optical Satellite Images
As SWIR is typically of a lower resolution compared to VNIR, this study proposes a multiresolution fully convolutional neural network (FCN) that can effectively detect clouds and snow in VNIR images.
Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data
The success of supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model.
Despeckling Polarimetric SAR Data Using a Multi-Stream Complex-Valued Fully Convolutional Network
To this aim, deep learning based approaches separate the real and imaginary components of the complex-valued covariance matrix and use them as independent channels in a standard convolutional neural networks.
Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework
The main idea of our framework is to learn to predict the location of key vertices of the buildings and connect them in sequence.
Recurrent Multiresolution Convolutional Networks for VHR Image Classification
The feedforward version of the network, called FuseNet, aims to match the resolution of the panchromatic and multispectral bands in a VHR image using convolutional layers with corresponding downsampling and upsampling operations.
