Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.Comment: 9 pages, 9 figures, 4 table

Aplicações de modelos de deep learning para monitoramento ambiental e agrícola no Brasil

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022.Algoritmos do novo campo de aprendizado de máquina conhecido como Deep Learning têm se popularizado recentemente, mostrando resultados superiores a modelos tradicionais em métodos de classificação e regressão. O histórico de sua utilização no campo do sensoriamento remoto ainda é breve, porém eles têm mostrado resultados similarmente superiores em processos como a classificação de uso e cobertura da terra e detecção de mudança. Esta tese teve como objetivo o desenvolvimento de metodologias utilizando estes algoritmos com um enfoque no monitoramento de alvos críticos no Brasil por via de imagens de satélite a fim de buscar modelos de alta precisão e acurácia para substituir metodologias utilizadas atualmente. Ao longo de seu desenvolvimento, foram produzidos três artigos onde foi avaliado o uso destes algoritmos para a detecção de três alvos distintos: (a) áreas queimadas no Cerrado brasileiro, (b) áreas desmatadas na região da Amazônia e (c) plantios de arroz no sul do Brasil. Apesar do objetivo similar na produção dos artigos, procurou-se distinguir suficientemente suas metodologias a fim de expandir o espaço metodológico conhecido para fornecer uma base teórica para facilitar e incentivar a adoção destes algoritmos em contexto nacional. O primeiro artigo avaliou diferentes dimensões de amostras para a classificação de áreas queimadas em imagens Landsat-8. O segundo artigo avaliou a utilização de séries temporais binárias de imagens Landsat para a detecção de novas áreas desmatadas entre os anos de 2017, 2018 e 2019. O último artigo utilizou imagens de radar Sentinel-1 (SAR) em uma série temporal contínua para a delimitação dos plantios de arroz no Rio Grande do Sul. Modelos similares foram utilizados em todos os artigos, porém certos modelos foram exclusivos a cada publicação, produzindo diferentes resultados. De maneira geral, os resultados encontrados mostram que algoritmos de Deep Learning são não só viáveis para detecção destes alvos mas também oferecem desempenho superior a métodos existentes na literatura, representando uma alternativa altamente eficiente para classificação e detecção de mudança dos alvos avaliados.Algorithms belonging to the new field of machine learning called Deep Learning have been gaining popularity recently, showing superior results when compared to traditional classification and regression methods. The history of their use in the field of remote sensing is not long, however they have been showing similarly superior results in processes such as land use classification and change detection. This thesis had as its objective the development of methodologies using these algorithms with a focus on monitoring critical targets in Brazil through satellite imagery in order to find high accuracy and precision models to substitute methods used currently. Through the development of this thesis, articles were produced evaluating their use for the detection of three distinct targets: (a) burnt areas in the Brazilian Cerrado, (b) deforested areas in the Amazon region and (c) rice fields in the south of Brazil. Despite the similar objective in the production of these articles, the methodologies in each of them was made sufficiently distinct in order to expand the methodological space known. The first article evaluated the use of differently sized samples to classify burnt areas in Landsat-8 imagery. The second article evaluated the use of binary Landsat time series to detect new deforested areas between the years of 2017, 2018 and 2019. The last article used continuous radar Sentinel-1 (SAR) time series to map rice fields in the state of Rio Grande do Sul. Similar models were used in all articles, however certain models were exclusive to each one. In general, the results show that not only are the Deep Learning models viable but also offer better results in comparison to other existing methods, representing an efficient alternative when it comes to the classification and change detection of the targets evaluated

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing