1,004 research outputs found
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
Very High Resolution (VHR) Satellite Imagery: Processing and Applications
Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing
A Multi-scale colour and Keypoint Density-based Approach for Visual Saliency Detection.
In the first seconds of observation of an image, several visual attention processes are involved in the identification of the visual targets that pop-out from the scene to our eyes. Saliency is the quality that makes certain regions of an image stand out from the visual field and grab our attention. Saliency detection models, inspired by visual cortex mechanisms, employ both colour and luminance features. Furthermore, both locations of pixels and presence of objects influence the Visual Attention processes. In this paper, we propose a new saliency method based on the combination of the distribution of interest points in the image with multiscale analysis, a centre bias module and a machine learning approach. We use perceptually uniform colour spaces to study how colour impacts on the extraction of saliency. To investigate eye-movements and assess the performances of saliency methods over object-based images, we conduct experimental sessions on our dataset ETTO (Eye Tracking Through Objects). Experiments show our approach to be accurate in the detection of saliency concerning state-of-the-art methods and accessible eye-movement datasets. The performances over object-based images are excellent and consistent on generic pictures. Besides, our work reveals interesting findings on some relationships between saliency and perceptually uniform colour spaces
Signal processing algorithms for enhanced image fusion performance and assessment
The dissertation presents several signal processing algorithms for image fusion in noisy multimodal
conditions. It introduces a novel image fusion method which performs well for image
sets heavily corrupted by noise. As opposed to current image fusion schemes, the method has
no requirements for a priori knowledge of the noise component. The image is decomposed with
Chebyshev polynomials (CP) being used as basis functions to perform fusion at feature level. The
properties of CP, namely fast convergence and smooth approximation, renders it ideal for heuristic
and indiscriminate denoising fusion tasks. Quantitative evaluation using objective fusion assessment
methods show favourable performance of the proposed scheme compared to previous efforts
on image fusion, notably in heavily corrupted images.
The approach is further improved by incorporating the advantages of CP with a state-of-the-art
fusion technique named independent component analysis (ICA), for joint-fusion processing
based on region saliency. Whilst CP fusion is robust under severe noise conditions, it is prone to
eliminating high frequency information of the images involved, thereby limiting image sharpness.
Fusion using ICA, on the other hand, performs well in transferring edges and other salient features
of the input images into the composite output. The combination of both methods, coupled with
several mathematical morphological operations in an algorithm fusion framework, is considered a
viable solution. Again, according to the quantitative metrics the results of our proposed approach
are very encouraging as far as joint fusion and denoising are concerned.
Another focus of this dissertation is on a novel metric for image fusion evaluation that is based
on texture. The conservation of background textural details is considered important in many fusion
applications as they help define the image depth and structure, which may prove crucial in
many surveillance and remote sensing applications. Our work aims to evaluate the performance of image fusion algorithms based on their ability to retain textural details from the fusion process.
This is done by utilising the gray-level co-occurrence matrix (GLCM) model to extract second-order
statistical features for the derivation of an image textural measure, which is then used to
replace the edge-based calculations in an objective-based fusion metric. Performance evaluation
on established fusion methods verifies that the proposed metric is viable, especially for multimodal
scenarios
VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts
Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9
Workshops Stream 1 10
Workshop Stream 2 11
Workshop Stream 3 12
Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14
Session 2 – Visualisation, communication & Teaching 27
Session 3 – Applying Machine Learning in Geosciences 36
Session 4 – Digital Outcrop Characterisation & Analysis 49
Session 5 – Airborne & Remote Mapping 58
Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69
Session 7 – Applications in Hydrology & Ecology 82
Poster Contributions 9
ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary Transformer
Deep learning (DL) has advanced the field of dense prediction, while
gradually dissolving the inherent barriers between different tasks. However,
most existing works focus on designing architectures and constructing visual
cues only for the specific task, which ignores the potential uniformity
introduced by the DL paradigm. In this paper, we attempt to construct a novel
\underline{ComP}lementary \underline{tr}ansformer, \textbf{ComPtr}, for diverse
bi-source dense prediction tasks. Specifically, unlike existing methods that
over-specialize in a single task or a subset of tasks, ComPtr starts from the
more general concept of bi-source dense prediction. Based on the basic
dependence on information complementarity, we propose consistency enhancement
and difference awareness components with which ComPtr can evacuate and collect
important visual semantic cues from different image sources for diverse tasks,
respectively. ComPtr treats different inputs equally and builds an efficient
dense interaction model in the form of sequence-to-sequence on top of the
transformer. This task-generic design provides a smooth foundation for
constructing the unified model that can simultaneously deal with various
bi-source information. In extensive experiments across several representative
vision tasks, i.e. remote sensing change detection, RGB-T crowd counting,
RGB-D/T salient object detection, and RGB-D semantic segmentation, the proposed
method consistently obtains favorable performance. The code will be available
at \url{https://github.com/lartpang/ComPtr}
An Evaluation of Deep Learning-Based Object Identification
Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study
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