711 research outputs found
A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture
Agricultural research is essential for increasing food production to meet the
requirements of an increasing population in the coming decades. Recently,
satellite technology has been improving rapidly and deep learning has seen much
success in generic computer vision tasks and many application areas which
presents an important opportunity to improve analysis of agricultural land.
Here we present a systematic review of 150 studies to find the current uses of
deep learning on satellite imagery for agricultural research. Although we
identify 5 categories of agricultural monitoring tasks, the majority of the
research interest is in crop segmentation and yield prediction. We found that,
when used, modern deep learning methods consistently outperformed traditional
machine learning across most tasks; the only exception was that Long Short-Term
Memory (LSTM) Recurrent Neural Networks did not consistently outperform Random
Forests (RF) for yield prediction. The reviewed studies have largely adopted
methodologies from generic computer vision, except for one major omission:
benchmark datasets are not utilised to evaluate models across studies, making
it difficult to compare results. Additionally, some studies have specifically
utilised the extra spectral resolution available in satellite imagery, but
other divergent properties of satellite images - such as the hugely different
scales of spatial patterns - are not being taken advantage of in the reviewed
studies.Comment: 25 pages, 2 figures and lots of large tables. Supplementary materials
section included here in main pd
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
Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification
A novel band selection and spatial noise reduction method for hyperspectral image classification.
As an essential reprocessing method, dimensionality reduction (DR) can reduce the data redundancy and improve the performance of hyperspectral image (HSI) classification. A novel unsupervised DR framework with feature interpretability, which integrates both band selection (BS) and spatial noise reduction method, is proposed to extract low-dimensional spectral-spatial features of HSI. We proposed a new Neighboring band Grouping and Normalized Matching Filter (NGNMF) for BS, which can reduce the data dimension whilst preserve the corresponding spectral information. An enhanced 2-D singular spectrum analysis (E2DSSA) method is also proposed to extract the spatial context and structural information from each selected band, aiming to decrease the intra-class variability and reduce the effect of noise in the spatial domain. The support vector machine (SVM) classifier is used to evaluate the effectiveness of the extracted spectral-spatial low-dimensional features. Experimental results on three publicly available HSI datasets have fully demonstrated the efficacy of the proposed NGNMF-E2DSSA method, which has surpassed a number of state-of-the-art DR methods
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Recent advances in artificial intelligence (AI) have significantly
intensified research in the geoscience and remote sensing (RS) field. AI
algorithms, especially deep learning-based ones, have been developed and
applied widely to RS data analysis. The successful application of AI covers
almost all aspects of Earth observation (EO) missions, from low-level vision
tasks like super-resolution, denoising and inpainting, to high-level vision
tasks like scene classification, object detection and semantic segmentation.
While AI techniques enable researchers to observe and understand the Earth more
accurately, the vulnerability and uncertainty of AI models deserve further
attention, considering that many geoscience and RS tasks are highly
safety-critical. This paper reviews the current development of AI security in
the geoscience and RS field, covering the following five important aspects:
adversarial attack, backdoor attack, federated learning, uncertainty and
explainability. Moreover, the potential opportunities and trends are discussed
to provide insights for future research. To the best of the authors' knowledge,
this paper is the first attempt to provide a systematic review of AI
security-related research in the geoscience and RS community. Available code
and datasets are also listed in the paper to move this vibrant field of
research forward
ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications
Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research
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
Artificial Neural Networks in Agriculture
Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible
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