@Article{WagnerSSHFLMYS:2022:KtSeHa,
               author = "Wagner, Fabien Hubert and Silva, Ricardo Dalagnol and S{\'a}nchez 
                         Ipia, Alber Hamersson and Hirye, Mayumi C. M. and Favrichon, 
                         Samuel and Lee, Jake H. and Mauceri, Steffen and Yang, Yan and 
                         Saatchi, Sassan",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade de S{\~a}o Paulo 
                         (USP)} and {NASA-Jet Propulsion Laboratory} and {NASA-Jet 
                         Propulsion Laboratory} and {NASA-Jet Propulsion Laboratory} and 
                         {NASA-Jet Propulsion Laboratory} and {NASA-Jet Propulsion 
                         Laboratory}",
                title = "K-textures, a self-supervised hard clustering deep learning 
                         algorithm for satellite image segmentation",
              journal = "Frontiers in Environmental Science",
                 year = "2022",
               volume = "10",
                pages = "e946729",
                month = "Sept.",
             keywords = "deep learning - artificial neural network, discrete optimization 
                         algorithm, landcover, planetscope satellite, segmentation (image 
                         processing), self-supervised segmentation, tensorflow (2), 
                         tropical forest.",
             abstract = "Deep learning self-supervised algorithms that can segment an image 
                         in a fixed number of hard clusters such as the k-means algorithm 
                         and with an end-to-end deep learning approach are still lacking. 
                         Here, we introduce the k-textures algorithm which provides 
                         self-supervised segmentation of a 4-band image (RGB-NIR) for a k 
                         number of classes. An example of its application on 
                         high-resolution Planet satellite imagery is given. Our algorithm 
                         shows that discrete search is feasible using convolutional neural 
                         networks (CNN) and gradient descent. The model detects k hard 
                         clustering classes represented in the model as k discrete binary 
                         masks and their associated k independently generated textures, 
                         which combined are a simulation of the original image. The 
                         similarity loss is the mean squared error between the features of 
                         the original and the simulated image, both extracted from the 
                         penultimate convolutional block of Keras imagenet pre-trained 
                         VGG-16 model and a custom feature extractor made with Planet data. 
                         The main advances of the k-textures model are: first, the k 
                         discrete binary masks are obtained inside the model using gradient 
                         descent. The model allows for the generation of discrete binary 
                         masks using a novel method using a hard sigmoid activation 
                         function. Second, it provides hard clustering classeseach pixel 
                         has only one class. Finally, in comparison to k-means, where each 
                         pixel is considered independently, here, contextual information is 
                         also considered and each class is not associated only with similar 
                         values in the color channels but with a texture. Our approach is 
                         designed to ease the production of training samples for satellite 
                         image segmentation and the k-textures architecture could be 
                         adapted to support different numbers of bands and for more complex 
                         self-segmentation tasks, such as object self-segmentation. The 
                         model codes and weights are available at 
                         https://doi.org/10.5281/zenodo.6359859.",
                  doi = "10.3389/fenvs.2022.946729",
                  url = "http://dx.doi.org/10.3389/fenvs.2022.946729",
                 issn = "2296-665X",
             language = "en",
           targetfile = "fenvs-10-946729.pdf",
        urlaccessdate = "11 maio 2024"
}