Papers by Victor Alhassan
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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In this thesis, we present an approach to automating the creation of land use and land cover (LUL... more In this thesis, we present an approach to automating the creation of land use and land cover (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural images. This work is important since the production of accurate and timely LULC maps is becoming essential to government and private companies that rely on them for large-scale monitoring of land resource changes. In this work, deep neural networks are trained to classify each pixel of a satellite image into one of a number of LULC classes. The presented deep neural networks are all pre-trained using the ImageNet LargeScale Visual Recognition Competition (ILSVRC) datasets and then fine-tuned using ∼19,000 Landsat 5/7 satellite images of resolution 224 × 224 taken of the Province of Manitoba in Canada. The initial results achieved was 88% global accuracy. Furthermore, we consider the use of state-of-the-art generative adversarial architecture and context module to impro...
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IFAC-PapersOnLine, 2021
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International Journal of Remote Sensing, 2019
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Neural Computing and Applications, 2019
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Papers by Victor Alhassan