Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognition

Autonomous navigation in underwater environments presents challenges due to factors such as light absorption and water turbidity, limiting the effectiveness of optical sensors. Sonar systems are commonly used for perception in underwater operations as they are unaffected by these limitations. Traditional computer vision algorithms are less effective when applied to sonar-generated acoustic images, while convolutional neural networks (CNNs) typically require large amounts of labeled training data that are often unavailable or difficult to acquire. To this end, we propose a novel compact deep sonar descriptor pipeline that can generalize to real scenarios while being trained exclusively on synthetic data. Our architecture is based on a ResNet18 back-end and a properly parameterized random Gaussian projection layer, whereas input sonar data is enhanced with standard ad-hoc normalization/prefiltering techniques. A customized synthetic data generation procedure is also presented. The proposed method has been evaluated extensively using both synthetic and publicly available real data, demonstrating its effectiveness compared to state-of-the-art methods.Comment: This paper has been accepted for publication at the 14th International Conference on Computer Vision Systems (ICVS 2023

Methods for Detecting Floodwater on Roadways from Ground Level Images

Recent research and statistics show that the frequency of flooding in the world has been increasing and impacting flood-prone communities severely. This natural disaster causes significant damages to human life and properties, inundates roads, overwhelms drainage systems, and disrupts essential services and economic activities. The focus of this dissertation is to use machine learning methods to automatically detect floodwater in images from ground level in support of the frequently impacted communities. The ground level images can be retrieved from multiple sources, including the ones that are taken by mobile phone cameras as communities record the state of their flooded streets. The model developed in this research processes these images in multiple levels. The first detection model investigates the presence of flood in images by developing and comparing image classifiers with various feature extractors. Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and pretrained convolutional neural networks are used as feature extractors. Then, decision trees, logistic regression, and K-Nearest Neighbors (K-NN) models are trained and tested for making predictions on floodwater presence in the image. Once the model detects flood in an image, it moves to the second layer to detect the presence of floodwater at a pixel level in each image. This pixel-level identification is achieved by semantic segmentation by using a super-pixel based prediction method and Fully Convolutional Neural Networks (FCNs). First, SLIC super-pixel method is used to create the super-pixels, then the same types of classifiers as the initial classification method are trained to predict the class of each super-pixel. Later, the FCN is trained end-to-end without any additional classifiers. Once these processes are done, images are segmented into regions of floodwater at pixel level. In both of the classification and semantic segmentation tasks, deep learning-based methods showed the best results. Once the model receives the confirmation of flood detection in image and pixel layers, it moves to the final task of finding the floodwater depth in images. This third and final layer of the model is critical as it can help officials deduce the severity of the flood at a given area. In order to detect the depth of the water and the severity of the flooding, the model processes the cars on streets that are in water and calculates the percentage of tires that are under water. This calculation is achieved with a mixture of deep learning and classical computer vision techniques. There are four main processes in this task: (i)-Semantic segmentation of the image into pixels that belong to background, floodwater, and wheels of vehicles. The segmentation is done by multiple FCN models that are trained with various base models. (ii)-Object detection models for detecting tires. The tires are identified by a You Only Look Once (YOLO) object detector. (iii)- Improvement of initial segmentation results. A U-Net like semantic segmentation network is proposed. It uses the tire patches from the object detector and the corresponding initial segmentation results, and it learns to fix the errors of the initial segmentation results. (iv)-Calculation of water depth as a ratio of the tire wheel under the water. This final task uses the improved segmentation results to identify the ellipses that correspond to the wheel parts of vehicles and utilizes two approaches listed below as part of a hybrid method: (i)-Using the improved segmentation results as they return the pixels belonging to the wheels. Boundaries of the wheels are found from this and used. (ii)-Finding arcs that belong to elliptical objects by applying a series of image processing methods. This method connects the arcs found to build larger structures such as two-piece (half ellipse), three-piece or four-piece (full) ellipses. Once the ellipse boundary is calculated using both methods, the ratio of the ellipse under floodwater can be calculated. This novel multi-model system allows us to attribute potential prediction errors to the different parts of the model such as semantic segmentation of the image or the calculation of the elliptical boundary. To verify the applicability of the proposed methods and to train the models, extensive hand-labeled datasets were created as part of this dissertation. The initial images were collected from the web, then the datasets were enriched by images created from virtual environments, simulations of neighborhoods under flood, using the Unity software. In conclusion, the proposed methods in this dissertation, as validated on the labeled datasets, can successfully classify images as a flood scene, semantically segment the regions of flood, and predict the depth of water to indicate severit

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization

Enhanced change detection performance based on deep despeckling of synthetic aperture radar images

Synthetic aperture radar (SAR) image change detection (CD) focuses on identifying changes between two images at different times for the same geographical region. Recently, several deep learning methods have been proposed for performing SAR based CD. However, speckle noise remains a major challenge for these methods. To address this, we propose a despeckling model (DM) that effectively suppresses speckle noise and enhances the performance of the existing CD methods. The proposed despeckling architecture is not only resilient to multi-temporal SAR acquired from one SAR imaging process (i.e., the same number of SAR images looks before and after the change) but also deals with any combination of single or multi-look images acquired prior and after the change. Moreover, as a second contribution, we have also proposed a loss function that effectively suppresses speckle noise, thereby improving the change detection accuracy. Both the despeckling model and the proposed tolerant noise loss function are evaluated extensively on three public real SAR datasets, achieving superior performance compared to existing state-of-the-art SAR CD methods in all datasets

D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Scalable computing for earth observation - Application on Sea Ice analysis

In recent years, Deep learning (DL) networks have shown considerable improvements and have become a preferred methodology in many different applications. These networks have outperformed other classical techniques, particularly in large data settings. In earth observation from the satellite field, for example, DL algorithms have demonstrated the ability to learn complicated nonlinear relationships in input data accurately. Thus, it contributed to advancement in this field. However, the training process of these networks has heavy computational overheads. The reason is two-fold: The sizable complexity of these networks and the high number of training samples needed to learn all parameters comprising these architectures. Although the quantity of training data enhances the accuracy of the trained models in general, the computational cost may restrict the amount of analysis that can be done. This issue is particularly critical in satellite remote sensing, where a myriad of satellites generate an enormous amount of data daily, and acquiring in-situ ground truth for building a large training dataset is a fundamental prerequisite. This dissertation considers various aspects of deep learning based sea ice monitoring from SAR data. In this application, labeling data is very costly and time-consuming. Also, in some cases, it is not even achievable due to challenges in establishing the required domain knowledge, specifically when it comes to monitoring Arctic Sea ice with Synthetic Aperture Radar (SAR), which is the application domain of this thesis. Because the Arctic is remote, has long dark seasons, and has a very dynamic weather system, the collection of reliable in-situ data is very demanding. In addition to the challenges of interpreting SAR data of sea ice, this issue makes SAR-based sea ice analysis with DL networks a complicated process. We propose novel DL methods to cope with the problems of scarce training data and address the computational cost of the training process. We analyze DL network capabilities based on self-designed architectures and learn strategies, such as transfer learning for sea ice classification. We also address the scarcity of training data by proposing a novel deep semi-supervised learning method based on SAR data for incorporating unlabeled data information into the training process. Finally, a new distributed DL method that can be used in a semi-supervised manner is proposed to address the computational complexity of deep neural network training