Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network—Mask R-CNN algorithm—to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising—the R e c a l l , P r e c i s i o n , and F 1 score values obtained were were 0.81 , 0.91 , and 0.86 , respectively. In the study site, the total of tree crowns delineated was 59,062 . These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions.

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Hyperspectral images provide fine details of the scene under analysis in terms of spectral information. This is due to the presence of contiguous bands that make possible to distinguish different objects even when they have similar colour and shape. However, neighbouring bands are highly correlated, and, besides, the high dimensionality of hyperspectral images brings a heavy burden on processing and also may cause the Hughes phenomenon. It is therefore advisable to make a band selection pre-processing prior to the classification task. Thus, this paper proposes a new supervised filter-based approach for band selection based on neural networks. For each class of the data set, a binary single-layer neural network classifier performs a classification between that class and the remainder of the data. After that, the bands related to the biggest and smallest weights are selected, so, the band selection process is class-oriented. This process iterates until the previously defined number of bands is achieved. A comparison with three state-of-the-art band selection approaches shows that the proposed method yields the best results in 43.33% of the cases even with greatly reduced training data size, whereas the competitors have achieved between 13.33% and 23.33% on the Botswana, KSC and Indian Pines datasets.

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This paper describes a methodology for using neural networks in an inverse heat conduction problem. Three neural network (NN) models are used to determine the initial temperature profile on a slab with adiabatic boundary condition, given a transient temperature distribution at a given time. This is an ill-posed 1D parabolic inverse problem, where the initial condition has to be estimated. Three neural network models addressed the problem: a feedforward network with backpropagation, radial basis functions (RBF), and cascade correlation. The input for the NN is the temperature profile obtained from a set of probes equally spaced in the one-dimensional domain. The NNs were trainned considering a 5% of noise in the experimental data. The training was performed considering 500 similar testfunctions and 500 different test-functions. Good reconstructions have been obtained with the proposed methodology. NOMENCLATURE ASE Average square error b k Bias employed in the NNs CasCor Cascate correlation NN f(x) Unknown nitial condition g(x) Activation function w ji Conection weight of a NN N(β m) Norm of the eigenfunction NN Neural network RBF Radial base function NN T(x,t) Temperature calculate) , (τ x T Experimental temperature X(β m x) eigenfunction α Regularization parameter β m Eigenvalue in Eq. (2) η Learning rate µ Random variable σ Standard deviation Ω Space domain