february, 2024

29feb4:00 pm5:30 pmGEOSERIES - Enhancing Critical Remote Sensing Applications With a Combined Optical and SAR ApproachOnline

Webinar EUSI-Umbra

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Event Details

EUSI is joined by Umbra in exploring the opportunities to gain holistic insights of ongoing situations through EUSI’s combined tasking of the world’s highest resolution optical and Synthetic Aperture Radar (SAR) satellite imagery. While both datasets have been widely used in civil, commercial and security applications, never could users schedule synchronized 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.

Responding to events on the ground requires time-critical intelligence from space.

  • What evacuation routes are currently blocked?
  • How many cargo planes are on the runway?

While VHR optical imagery can provide you with ample visual details, it falls short at night or in cloudy weather. SAR satellites can see through the clouds and darkness to provide guaranteed images of unfolding events.

In this webinar, we will discuss the latest in SAR technology with Umbra and how to schedule “Double Shot” tasking with EUSI, so you always get the insights you need regardless of conditions.

Time

(Thursday) 4:00 pm - 5:30 pm

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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