A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval

This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). In object and scene analysis, deep neural nets are capable of learning a hierarchical chain of abstraction from pixel inputs to concise and descriptive representations. The current work explores this capacity in the realm of document analysis, and confirms that this representation strategy is superior to a variety of popular hand-crafted alternatives. Experiments also show that (i) features extracted from CNNs are robust to compression, (ii) CNNs trained on non-document images transfer well to document analysis tasks, and (iii) enforcing region-specific feature-learning is unnecessary given sufficient training data. This work also makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis

Hahmontunnistus merenkulkusovelluksessa syviä neuroverkkoja käyttäen

The aim of this thesis was to study object recognition with the state of the art methods in order to evaluate their potential for the sector of autonomous maritime logistics. In autonomous maritime transportation, object localization and recognition is crucial for safe and efficient traffic flow. In this study, object recognition was studied by training deep convolutional neural networks for image classification and by evaluating their classification and computational performance. In machine learning, a classification algorithm is trained with supervised learning with a dataset of input-output examples. Object recognition is a classification task where objects are classified from images. In deep learning, deep neural networks with multiple layers learn hierarchical representations of the data. For training, they require more computation and data than traditional machine learning methods. In the past years, more and more data has become available, and the computation capacity has increased dramatically. Therefore, deep neural networks have outperformed traditional machine learning algorithms in many tasks, such as object recognition. The best results in object recognition are achieved using deep convolutional neural networks. In the experiments, deep convolutional neural networks were trained for image classification with Rolls-Royce Maritime Image (RRMI) dataset. Small-CNN architecture was generated and trained with random hyperparameter search approach using random weight initialization whereas VGG16, ResNet50 and MobileNet architectures were trained with transfer learning. The classification and computational performances of the models were measured. Transfer learning approach proved to improve classification performance. The VGG16 achieved the best accuracy of 84.0% for the dataset. The best average class accuracy of 78.4% was achieved with the ResNet50. The computational performance of the models was evaluated by measuring the time required for image classification with a CPU and GPU in order to evaluate their potential for a real-time object localization and recognition system. With the GPU, the models were much faster and performed in 3.6-16.0 milliseconds

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

A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks

Transfer Learning (TL) plays a crucial role when a given dataset has insufficient labeled examples to train an accurate model. In such scenarios, the knowledge accumulated within a model pre-trained on a source dataset can be transferred to a target dataset, resulting in the improvement of the target model. Though TL is found to be successful in the realm of image-based applications, its impact and practical use in Natural Language Processing (NLP) applications is still a subject of research. Due to their hierarchical architecture, Deep Neural Networks (DNN) provide flexibility and customization in adjusting their parameters and depth of layers, thereby forming an apt area for exploiting the use of TL. In this paper, we report the results and conclusions obtained from extensive empirical experiments using a Convolutional Neural Network (CNN) and try to uncover thumb rules to ensure a successful positive transfer. In addition, we also highlight the flawed means that could lead to a negative transfer. We explore the transferability of various layers and describe the effect of varying hyper-parameters on the transfer performance. Also, we present a comparison of accuracy value and model size against state-of-the-art methods. Finally, we derive inferences from the empirical results and provide best practices to achieve a successful positive transfer.Comment: 9 pages, 2 figures, accepted in SDM 201