diff --git a/doc/EXAMPLES.md b/doc/EXAMPLES.md index 0c1dce39eea67a2c19c75cc8f1992d54d5b50d36..bea6b2a8ebd77972c77d982ad2dd25ab14a6484f 100644 --- a/doc/EXAMPLES.md +++ b/doc/EXAMPLES.md @@ -3,6 +3,13 @@ Some examples of ready-to-use deep learning architectures built with the TensorFlow API from python. All models used are provided in this directory. +**Table of Contents** +1. [Simple CNN](#Simple-CNN) +2. [Fully convolutional network](#Fully-convolutional-network) +3. [M3Fusion Model](#M3Fusion-Model) +4. [Maggiori model](#Maggiori-model) +5. [Fully convolutional network with separate Pan/MS channels](#Fully-convolutional-network-with-separate-Pan/MS-channels) + ## Simple CNN This simple model estimates the class of an input patch of image. @@ -115,9 +122,9 @@ otbcli_TensorflowModelServe \ -out $output_classif ``` -## M3 Model +## M3Fusion Model -The M3 model (stands for MultiScale/Multimodal/Multitemporal satellite data fusion) is a model designed to input time series and very high resolution images. +The M3Fusion model (stands for MultiScale/Multimodal/Multitemporal satellite data fusion) is a model designed to input time series and very high resolution images. Benedetti, P., Ienco, D., Gaetano, R., Ose, K., Pensa, R. G., & Dupuy, S. (2018). _M3Fusion: A Deep Learning Architecture for Multiscale Multimodal Multitemporal Satellite Data Fusion_. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12), 4939-4949.