From 2e192a2f62b340fe83ef871667e09436cffd4bae Mon Sep 17 00:00:00 2001 From: isaac <22203655+isaaccorley@users.noreply.github.com> Date: Fri, 23 Jul 2021 22:48:49 -0500 Subject: [PATCH] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f3395d2..3c24c95 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ pip install git+https://github.com/isaaccorley/torchrs -The [PROBA-V Super Resolution Challenge Dataset](https://kelvins.esa.int/proba-v-super-resolution/home/) is a Multi-image Super Resolution (MISR) dataset of images taken by the [ESA PROBA-Vegetation satellite](https://earth.esa.int/eogateway/missions/proba-v). The dataset contains sets of 300m low resolution (LR) images which can be used to generate 100m high resolution (HR) images. In addition to LR and HR imagery, the dataset contains Quality Masks (QM) for each LR image and a Status Mask (SM) for each HR image. Imagery are available for the Near-Infrared (NIR) and Red bands. The PROBA-V contains sensors which take imagery at 100 and 300 meter spatial resolutions with 5 and 1 day revisit rates, respectively. Generating high resolution estimates using low resolution imagery would effectively increase the frequency at which high resolution imagery is available for vegatation monitoring. +The [PROBA-V Super Resolution Challenge Dataset](https://kelvins.esa.int/proba-v-super-resolution/home/) is a Multi-image Super Resolution (MISR) dataset of images taken by the [ESA PROBA-Vegetation satellite](https://earth.esa.int/eogateway/missions/proba-v). The dataset contains sets of 300m low resolution (LR) images which can be used to generate single 100m high resolution (HR) images for both Near Infrared (NIR) and Red bands. In addition, Quality Masks (QM) for each LR image and Status Masks (SM) for each HR image are available. The PROBA-V contains sensors which take imagery at 100m and 300m spatial resolutions with 5 and 1 day revisit rates, respectively. Generating high resolution imagery estimates would effectively increase the frequency at which HR imagery is available for vegetation monitoring. The dataset can be downloaded using the `scripts/download_probav.sh` script and then used as below: @@ -78,4 +78,4 @@ sr = model(x) # (1, 1, 384, 384) ``` $ pytest -ra -``` \ No newline at end of file +```