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

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS

Face recognition has attracted many researchers’ attention compared to other biometrics due to its non-intrusive and friendly nature. Although several methods for 2D face recognition have been proposed so far, there are still some challenges related to the 2D face including illumination, pose variation, and facial expression. In the last few decades, 3D face research area has become more interesting since shape and geometry information are used to handle challenges from 2D faces. Existing algorithms for face recognition are divided into three different categories: holistic feature-based, local feature-based, and hybrid methods. According to the literature, local features have shown better performance relative to holistic feature-based methods under expression and occlusion challenges. In this dissertation, local feature-based methods for 3D face recognition have been studied and surveyed. In the survey, local methods are classified into three broad categories which consist of keypoint-based, curve-based, and local surface-based methods. Inspired by keypoint-based methods which are effective to handle partial occlusion, structural context descriptor on pyramidal shape maps and texture image has been proposed in a multimodal scheme. Score-level fusion is used to combine keypoints’ matching score in both texture and shape modalities. The survey shows local surface-based methods are efficient to handle facial expression. Accordingly, a local derivative pattern is introduced to extract distinct features from depth map in this work. In addition, the local derivative pattern is applied on surface normals. Most 3D face recognition algorithms are focused to utilize the depth information to detect and extract features. Compared to depth maps, surface normals of each point can determine the facial surface orientation, which provides an efficient facial surface representation to extract distinct features for recognition task. An Extreme Learning Machine (ELM)-based auto-encoder is used to make the feature space more discriminative. Expression and occlusion robust analysis using the information from the normal maps are investigated by dividing the facial region into patches. A novel hybrid classifier is proposed to combine Sparse Representation Classifier (SRC) and ELM classifier in a weighted scheme. The proposed algorithms have been evaluated on four widely used 3D face databases; FRGC, Bosphorus, Bu-3DFE, and 3D-TEC. The experimental results illustrate the effectiveness of the proposed approaches. The main contribution of this work lies in identification and analysis of effective local features and a classification method for improving 3D face recognition performance

Smart video surveillance of pedestrians : fixed, aerial, and multi-camera methods

Crowd analysis from video footage is an active research topic in the field of computer vision. Crowds can be analaysed using different approaches, depending on their characteristics. Furthermore, analysis can be performed from footage obtained through different sources. Fixed CCTV cameras can be used, as well as cameras mounted on moving vehicles. To begin, a literature review is provided, where research works in the the fields of crowd analysis, as well as object and people tracking, occlusion handling, multi-view and sensor fusion, and multi-target tracking are analyses and compared, and their advantages and limitations highlighted. Following that, the three contributions of this thesis are presented: in a first study, crowds will be classified based on various cues (i.e. density, entropy), so that the best approaches to further analyse behaviour can be selected; then, some of the challenges of individual target tracking from aerial video footage will be tackled; finally, a study on the analysis of groups of people from multiple cameras is proposed. The analysis entails the movements of people and objects in the scene. The idea is to track as many people as possible within the crowd, and to be able to obtain knowledge from their movements, as a group, and to classify different types of scenes. An additional contribution of this thesis, are two novel datasets: on the one hand, a first set to test the proposed aerial video analysis methods; on the other, a second to validate the third study, that is, with groups of people recorded from multiple overlapping cameras performing different actions

Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

Deep-learning frameworks have made remarkable progress thanks to the creation of large annotated datasets such as ImageNet, which has over one million training images. Although this works well for color (RGB) imagery, labeled datasets for other sensor modalities (e.g., multispectral and hyperspectral) are minuscule in comparison. This is because annotated datasets are expensive and man-power intensive to complete; and since this would be impractical to accomplish for each type of sensor, current state-of-the-art approaches in computer vision are not ideal for remote sensing problems. The shortage of annotated remote sensing imagery beyond the visual spectrum has forced researchers to embrace unsupervised feature extracting frameworks. These features are learned on a per-image basis, so they tend to not generalize well across other datasets. In this dissertation, we propose three new strategies for learning feature extracting frameworks with only a small quantity of annotated image data; including 1) self-taught feature learning, 2) domain adaptation with synthetic imagery, and 3) semi-supervised classification. ``Self-taught\u27\u27 feature learning frameworks are trained with large quantities of unlabeled imagery, and then these networks extract spatial-spectral features from annotated data for supervised classification. Synthetic remote sensing imagery can be used to boot-strap a deep convolutional neural network, and then we can fine-tune the network with real imagery. Semi-supervised classifiers prevent overfitting by jointly optimizing the supervised classification task along side one or more unsupervised learning tasks (i.e., reconstruction). Although obtaining large quantities of annotated image data would be ideal, our work shows that we can make due with less cost-prohibitive methods which are more practical to the end-user

Automatic inference of latent emotion from spontaneous facial micro-expressions

Emotional states exert a profound influence on individuals' overall well-being, impacting them both physically and psychologically. Accurate recognition and comprehension of human emotions represent a crucial area of scientific exploration. Facial expressions, vocal cues, body language, and physiological responses provide valuable insights into an individual's emotional state, with facial expressions being universally recognised as dependable indicators of emotions. This thesis centres around three vital research aspects concerning the automated inference of latent emotions from spontaneous facial micro-expressions, seeking to enhance and refine our understanding of this complex domain. Firstly, the research aims to detect and analyse activated Action Units (AUs) during the occurrence of micro-expressions. AUs correspond to facial muscle movements. Although previous studies have established links between AUs and conventional facial expressions, no such connections have been explored for micro-expressions. Therefore, this thesis develops computer vision techniques to automatically detect activated AUs in micro-expressions, bridging a gap in existing studies. Secondly, the study explores the evolution of micro-expression recognition techniques, ranging from early handcrafted feature-based approaches to modern deep-learning methods. These approaches have significantly contributed to the field of automatic emotion recognition. However, existing methods primarily focus on capturing local spatial relationships, neglecting global relationships between different facial regions. To address this limitation, a novel third-generation architecture is proposed. This architecture can concurrently capture both short and long-range spatiotemporal relationships in micro-expression data, aiming to enhance the accuracy of automatic emotion recognition and improve our understanding of micro-expressions. Lastly, the thesis investigates the integration of multimodal signals to enhance emotion recognition accuracy. Depth information complements conventional RGB data by providing enhanced spatial features for analysis, while the integration of physiological signals with facial micro-expressions improves emotion discrimination. By incorporating multimodal data, the objective is to enhance machines' understanding of latent emotions and improve latent emotion recognition accuracy in spontaneous micro-expression analysis