{'fraction_non_alphanumeric': 0.02564102564102564, 'fraction_numerical': 0.0039447731755424065, 'mean_word_length': 5.3153526970954355, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Convolutional Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Images

The recent impulse in development of artificial intelligence (AI) methodologies has simplified the application of this in multiple research areas. This simplification was not favorable before, due to the limitations in dimensionality, processing time, computational resources, among others. Working with multispectral remote sensing (RS) images, in an artificial neural network (NN) was quite complex. Due the methods used required millions of processes that took a long time to be executed and produce competitive results compared with the state of the art (SoA). Deep learning (DL) strategies have been applied to alleviate these limitations and have greatly improved the use of neural networks. Therefore, this paper presents the analysis of DL-NNs to perform semantic segmentation of multispectral RS images. Images are captured by the constellation of satellites Sentinel-2 from the European Space Agency. The objective of this research is to classify each pixel of a scene into five categories: 1-vegetation, 2-soil, 3-water, 4-clouds and 5-cloud shadows. The selection of spectral bands for the formation of input datasets for segmentation of these classes is very important. The spectral signatures of each material aid to discern among several classes. Results presented in this work, show that the AI strategy proposed offer better accuracy segmentation than other methods of the SoA in competitive processing time.