On August 13, 2014, DigitalGlobe launched WorldView-3 into orbit.
On August 19, a mere six days after launch, they completed commissioning the satellite bus and opened the door on the main telescope to begin imagery testing on the entire suite of WorldView-3’s 27 super-spectral bands.
We are pleased to share several WorldView-3 image examples from the first image collection taken with the new satellite over Madrid, Spain, to show our customers the potential of this new satellite. Because of the regulatory restrictions, the images can’t yet display the 30 cm native resolution data, so we’re sharing imagery resampled to 40 cm below.
For the first time in history, users can schedule synchronised collections of 25 cm SAR and 30 cm Near Real-Time optical imagery to mitigate weather and gain deeper insights of events unfolding on the ground. This is especially valuable for Emergency Response, GEOINT and other applications.
Satellite sensors captured unique architecture, breathtaking nature and centuries of history. Explore the Colloseum, La Sagrada Familia, the Leaning Tower of Pisa, and other landmarks.
Off Nadir Angle (ONA) plays a crucial role in the quality of optical satellite imagery. It influences its resolution and clarity, decides the visibility of features, and makes it easier or harder to identify objects. Moreover, ONA is used to create stereo imagery and 3D models of the Earth’s surface. Read on to learn more.
Sun glint occurs when sunlight reflects off water or another reflective surface at the satellite sensor, creating a bright glare in the image. That can make it impossible to extract useful information from satellite imagery. What do we do to minimise its impact?
UNet architecture for semantic segmentation with ResNet34 as encoder or feature extraction part. ResNet34 is used as an encoder or feature extractor in the contracting path and the corresponding symmetric expanding path predicts the dense segmentation output.
UNet architecture for semantic segmentation with VGG16 as the encoder or feature extractor. VGG16 is used as an encoder or feature extractor in the contracting path and the corresponding symmetric expanding path predicts the dense segmentation output.
In this model, ResNet34 is used for feature extraction and the FCN operation remains as is. The feature of ResNet architecture is exploited where just like VGG, as the number of filters double, the feature map size gets halved. This gives a similarity to VGG and ResNet architecture while supporting deeper architecture and addressing the issue of vanishing gradients while also being faster. The fully connected layer at the output of ResNet34 is not used and instead converted to fully convolutional layer by means of 1×1 convolution.
In this model, VGG16 is used for feature extraction which also performs the function of an encoder. The fully connected layer of the VGG16 is not used and instead converted to fully convolutional layer by means of 1×1 convolution.