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

Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances

Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. More than 300 papers are covered in this review. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision, and systematically review the corresponding methods developed in a hierarchical division manner. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD. Future research directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than 300 papers relevant to the RSOD filed were reviewed in this surve

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

TreeFormer: a Semi-Supervised Transformer-based Framework for Tree Counting from a Single High Resolution Image

Automatic tree density estimation and counting using single aerial and satellite images is a challenging task in photogrammetry and remote sensing, yet has an important role in forest management. In this paper, we propose the first semisupervised transformer-based framework for tree counting which reduces the expensive tree annotations for remote sensing images. Our method, termed as TreeFormer, first develops a pyramid tree representation module based on transformer blocks to extract multi-scale features during the encoding stage. Contextual attention-based feature fusion and tree density regressor modules are further designed to utilize the robust features from the encoder to estimate tree density maps in the decoder. Moreover, we propose a pyramid learning strategy that includes local tree density consistency and local tree count ranking losses to utilize unlabeled images into the training process. Finally, the tree counter token is introduced to regulate the network by computing the global tree counts for both labeled and unlabeled images. Our model was evaluated on two benchmark tree counting datasets, Jiangsu, and Yosemite, as well as a new dataset, KCL-London, created by ourselves. Our TreeFormer outperforms the state of the art semi-supervised methods under the same setting and exceeds the fully-supervised methods using the same number of labeled images. The codes and datasets are available at https://github.com/HAAClassic/TreeFormer.Comment: Accepted in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN

Understanding cities with machine eyes: A review of deep computer vision in urban analytics

Modelling urban systems has interested planners and modellers for decades. Different models have been achieved relying on mathematics, cellular automation, complexity, and scaling. While most of these models tend to be a simplification of reality, today within the paradigm shifts of artificial intelligence across the different fields of science, the applications of computer vision show promising potential in understanding the realistic dynamics of cities. While cities are complex by nature, computer vision shows progress in tackling a variety of complex physical and non-physical visual tasks. In this article, we review the tasks and algorithms of computer vision and their applications in understanding cities. We attempt to subdivide computer vision algorithms into tasks, and cities into layers to show evidence of where computer vision is intensively applied and where further research is needed. We focus on highlighting the potential role of computer vision in understanding urban systems related to the built environment, natural environment, human interaction, transportation, and infrastructure. After showing the diversity of computer vision algorithms and applications, the challenges that remain in understanding the integration between these different layers of cities and their interactions with one another relying on deep learning and computer vision. We also show recommendations for practice and policy-making towards reaching AI-generated urban policies

PEDESTRIAN SEGMENTATION FROM COMPLEX BACKGROUND BASED ON PREDEFINED POSE FIELDS AND PROBABILISTIC RELAXATION

The wide use of cameras enables the availability of a large amount of image frames that can be used for people counting or to monitor crowds or single individuals for security purposes. These applications require both, object detection and tracking. This task has shown to be challenging due to problems such as occlusion, deformation, motion blur, and scale variation. One alternative to perform tracking is based on the comparison of features extracted for the individual objects from the image. For this purpose, it is necessary to identify the object of interest, a human image, from the rest of the scene. This paper introduces a method to perform the separation of human bodies from images with changing backgrounds. The method is based on image segmentation, the analysis of the possible pose, and a final refinement step based on probabilistic relaxation. It is the first work we are aware that probabilistic fields computed from human pose figures are combined with an improvement step of relaxation for pedestrian segmentation. The proposed method is evaluated using different image series and the results show that it can work efficiently, but it is dependent on some parameters to be set according to the image contrast and scale. Tests show accuracies above 71%. The method performs well in other datasets, where it achieves results comparable to stateof-the-art approaches

Intelligent Data Analytics using Deep Learning for Data Science

Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from various data sources. The proposed intelligent data analytics framework employs a novel strategy for improving data representation learning by incorporating supplemental data from various sources and structures. First, the research presents a multi-source fusion approach that utilizes confident learning techniques to improve the data quality from many noisy sources. Meta-learning methods based on advanced techniques such as the mixture of experts and differential evolution combine the predictive capacity of individual learners with a gating mechanism, ensuring that only the most trustworthy features or predictions are integrated to train the model. Then, a Multi-Level Convolutional Fusion is presented to train a model on the correspondence between local-global deep feature interactions to identify easily confused samples of different classes. The convolutional fusion is further enhanced with the power of Graph Transformers, aggregating the relevant neighboring features in graph-based input data structures and achieving state-of-the-art performance on a large-scale building damage dataset. Finally, weakly-supervised strategies, noise regularization, and label propagation are proposed to train a model on sparse input labeled data, ensuring the model\u27s robustness to errors and supporting the automatic expansion of the training set. The suggested approaches outperformed competing strategies in effectively training a model on a large-scale dataset of 500k photos, with just about 7% of the images annotated by a human. The proposed framework\u27s capabilities have benefited various data science applications, including fluid dynamics, geometric morphometrics, building damage classification from satellite pictures, disaster scene description, and storm-surge visualization

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