An Efficient Algorithm for Earth Surface Interpretation from Satellite Imagery

Many image segmentation algorithms are available but most of them are not fit for interpretation of satellite images. Mean-shift algorithm has been used in many recent researches as a promising image segmentation technique, which has the speed at O(kn2) where n is the number of data points and k is the number of average iteration steps for each data point. This method computes using a brute-force in the iteration of a pixel to compare with the region it is in. This paper proposes a novel algorithm named First-order Neighborhood Mean-shift (FNM) segmentation, which is enhanced from Mean-shift segmentation. This algorithm provides information about the relationship of a pixel with its neighbors; and makes them fall into the same region which improve the speed to O(kn). In this experiment, FNM were compared to well-known algorithms, i.e., K-mean (KM), Constrained K-mean (CKM), Adaptive K-mean (AKM), Fuzzy C-mean (FCM) and Mean-shift (MS) using the reference map from Landsat. FNM provided better results in terms of overall error and correctness criteria

Road Segmentation in High-Resolution Images Using Deep Residual Networks

Automatic road detection from remote sensing images is a vital application for traffic management, urban planning, and disaster management. The presence of occlusions like shadows of buildings, trees, and flyovers in high-resolution images and miss-classifications in databases create obstacles in the road detection task. Therefore, an automatic road detection system is required to detect roads in the presence of occlusions. This paper presents a deep convolutional neural network to address the problem of road detection, consisting of an encoder-decoder architecture. The architecture contains a U-Network with residual blocks. U-Network allows the transfer of low-level features to the high-level, helping the network to learn low-level details. Residual blocks help maintain the network's training performance, which may deteriorate due to a deep network. The encoder and decoder structures generate a feature map and classify pixels into road and non-road classes, respectively. Experimentation was performed on the Massachusetts road dataset. The results showed that the proposed model gave better accuracy than current state-of-the-art methods

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Detecção de estradas rurais em imagens Planet usando rede convolutional U-Net

Orientador: Prof. Dr. Jorge Antônio Silva CentenoCoorientador: Dr. Mario Ernesto Jijón PalmaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 25/08/2023Inclui referênciasResumo: O Brasil, um dos países mais extensos do mundo, possui uma significativa parcela de vias de rodagem situada em ambiente rural, sem a devida manutenção, o que dificulta a extensão de serviços à população rural. Muitas dessas estradas desempenham um papel fundamental na gestão territorial, uma vez que são responsáveis pelo escoamento da produção agrícola do interior do país e pela conectividade das comunidades rurais. A manutenção destas estradas e sua exploração para a extensão de serviços básicos, como energia e água, é somente possível com uma adequada atualização cartográfica da rede viária. Mais recentemente, o uso de métodos de aprendizado profundo para a análise de imagens orbitais tem crescido significativamente. Dentro desta nova realidade, esta pesquisa propõe uma abordagem baseada em técnicas de sensoriamento remoto aliadas a ferramentas de inteligência artificial, com o intuito de contribuir para solucionar o problema do mapeamento de estradas em áreas rurais. Para isto, se propõe o uso das redes convolucionais. Utilizando a arquitetura U-Net, foi possível identificar um potencial promissor na detecção de estradas rurais em imagens da constelação Planet. A taxa de detecção alcançada foi notável, atingindo uma acurácia de 92%. Contudo, é importante ressaltar a necessidade de aprimoramentos, visto que outras métricas de avaliação, como a precisão (76,66%) e o f1-score (69,48%), indicam margem para otimização dos parâmetros utilizados. No estudo também é feita uma análise comparativa entre o uso dos interpretadores na nuvem, do Google Colab (em ambiente virtual) e Pyzo (em ambiente local, utilizando o computador desktop/workstation fornecido pela UFPR). Verificou-se que o Colab apresenta vantagens em termos de custo e acesso a recursos de processamento. Entretanto, é relevante destacar que o uso do Colab também traz consigo algumas limitações, as quais requerem uma abordagem cuidadosa ao ajustar a complexidade do modelo e o tamanho do conjunto de dados.Abstract: Brazil, one of the largest countries in the world, has a significant number of roads in rural areas that are not properly maintained, making it difficult to extend services to the rural population. Many of these roads play a fundamental role in land management, as they are responsible for transporting agricultural produce from the interior of the country and for connecting rural communities. Maintaining these roads and exploiting them to extend basic services, such as energy and water, is only possible with a proper cartographic update of the road network. More recently, the use of deep learning methods to analyse orbital images has grown significantly. Within this new reality, this research proposes an approach based on remote sensing techniques combined with artificial intelligence tools, with the aim of helping to solve the problem of mapping roads in rural areas. To this end, the use of convolutional networks is proposed. Using the U-Net architecture, it was possible to identify promising potential for detecting rural roads in images from the Planet constellation. The detection rate achieved was remarkable, reaching an accuracy of 92 per cent. However, it is important to highlight the need for improvement, since other evaluation metrics, such as accuracy (76.66%) and f1-score (69.48%), indicate room for optimization of the parameters used. The study also makes a comparative analysis between the use of interpreters in the cloud, Google Colab (in a virtual environment) and Pyzo (in a local environment, using the desktop/workstation computer provided by UFPR). Colab was found to have advantages in terms of cost and access to processing resources. However, it is important to emphasize that the use of Colab also brings with it some limitations, which require a careful approach when adjusting the complexity of the model and the size of the data set

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing