A hybrid CUDA, OpenMP, and MPI parallel TCA-based domain adaptation for classification of very high-resolution remote sensing images

Domain Adaptation (DA) is a technique that aims at extracting information from a labeled remote sensing image to allow classifying a different image obtained by the same sensor but at a different geographical location. This is a very complex problem from the computational point of view, specially due to the very high-resolution of multispectral images. TCANet is a deep learning neural network for DA classification problems that has been proven as very accurate for solving them. TCANet consists of several stages based on the application of convolutional filters obtained through Transfer Component Analysis (TCA) computed over the input images. It does not require backpropagation training, in contrast to the usual CNN-based networks, as the convolutional filters are directly computed based on the TCA transform applied over the training samples. In this paper, a hybrid parallel TCA-based domain adaptation technique for solving the classification of very high-resolution multispectral images is presented. It is designed for efficient execution on a multi-node computer by using Message Passing Interface (MPI), exploiting the available Graphical Processing Units (GPUs), and making efficient use of each multicore node by using Open Multi-Processing (OpenMP). As a result, an accurate DA technique from the point of view of classification and with high speedup values over the sequential version is obtained, increasing the applicability of the technique to real problemsOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported in part by the Ministerio de Ciencia e Innovación, Government of Spain (grant numbers PID2019-104834GB-I00 and TED2021-130367B-I00), the Consellería de Educación, Universidade e Formación Profesional (grant number 2019–2022 ED431G-2019/04 and 2021–2024 ED431C 2022/16), and by the Junta de Castilla y León (project VA226P20 (PROPHET II Project)). All are co-funded by the European Regional Development Fund (ERDF)S

Deep Learning Based Classification Techniques for Hyperspectral Images in Real Time

Remote sensing can be defined as the acquisition of information from a given scene without coming into physical contact with it, through the use of sensors, mainly located on aerial platforms, which capture information in different ranges of the electromagnetic spectrum. The objective of this thesis is the development of efficient schemes, based on the use of deep learning neural networks, for the classification of remotely sensed multi and hyperspectral land cover images. Efficient schemes are those that are capable of obtaining good results in terms of classification accuracy and that can be computed in a reasonable amount of time depending on the task performed. Regarding computational platforms, multicore architectures and Graphics Processing Units (GPUs) will be considered