Worldview Legion
From Maxar Technologies

Legion Constellation 3

The future of earth observation

29 cm

Very High Resolution

The highest resolution commercial imagery in the world

15

Daily
Revisits

Sun synchronous and mid inclination orbits give more collection opportunities

8

Spectral Bands

The most valuable spectral bands to see beyond the naked eye

Designed and built by Maxar Technologies, WorldView Legion is the next generation of VHR optical satellites. Launching in 2022, the WorldView Legion constellation will contain six high-performance satellites that deliver continuity for existing customer missions and dramatically expand revisit over high-interest areas to better inform critical and time-sensitive decisions.

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Architecture of ResNet34-UNet model

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.

Architecture of VGG16-UNet model

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.

Architecture of ResNet34-FCN model

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.

Architecture of VGG16-FCN model

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.

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