WorldView-3

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wORLDVIEW-3 WorldView-3 was launched on August 13 2014, and truly set a new standard for commercial satellites in-orbit. With 29 spectral sensors on-board, the…

Mapping

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MAPPING Solutions Vital for infrastructure, transportation and emergency response applications, our advanced  mapping solutions mean you spend less time making maps and more time…

SWIR

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SWIR 3.7 m Short-Wave Infrared Satellite Imagery Employing valuable sensors in order to see beyond the capabilities of the human eye. Free Samples +…

Maritime

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Satellite Imagery For Maritime Unrestricted Access To The Ocean Detect Marine Vessels Identify and track ships using 30 cm resolution and multiple daily collections…

Intelligent Collection Planning

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Intelligent Collection Planning (ICP) The key to more efficient collection of large areas and difficult weather conditions Superior TASKING for your project Scalable Tasking…

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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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