Neyman-Pearson Criterion-Based Change Detection Methods for Wavelength-Resolution SAR Image Stacks

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

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A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

Remote Sensing ◽

10.3390/rs13152869 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2869

Author(s):

MohammadAli Hemati ◽

Mahdi Hasanlou ◽

Masoud Mahdianpari ◽

Fariba Mohammadimanesh

Keyword(s):

Time Series ◽

Change Detection ◽

Policy Development ◽

Atmospheric Correction ◽

Open Data ◽

Detection Methods ◽

Landsat 8 ◽

Landsat Images ◽

Detection Analysis ◽

The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060367 ◽

2021 ◽

Vol 10 (6) ◽

pp. 367

Author(s):

Simoni Alexiou ◽

Georgios Deligiannakis ◽

Aggelos Pallikarakis ◽

Ioannis Papanikolaou ◽

Emmanouil Psomiadis ◽

...

Keyword(s):

Soil Erosion ◽

Change Detection ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Mean Value ◽

Detection Methods ◽

Mountainous Catchments ◽

Aerial Vehicle ◽

Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

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Navigational Data Change Detection Based on Rasterizing Features & Attributes

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.256-259.2279 ◽

2012 ◽

Vol 256-259 ◽

pp. 2279-2284

Author(s):

Lian Ying Li ◽

Zhang Huang ◽

Xiao Lan Xu

Keyword(s):

Change Detection ◽

Detection Methods ◽

Vehicle Navigation ◽

Digital Map ◽

Navigation Data ◽

Data Production ◽

History Of ◽

Map Data ◽

Data Updating

A necessary updating degree is vital for the digital map data in a vehicle navigation system. Only when the digital map data are well updated, can the quality of the navigation be assured. Today the companies devoting to the production of digital map data for vehicle navigation have to cost much labor, material and capital to collect and update data in order to maintain a necessary updating degree. Throughout the history of electronic navigation data updating, they have made considerable progress both on the methods and processes of data production, and the way of map management. Updating from the CD to the network, from the wired to the wireless, from the replacing to the incremental way, each of the technical changes is a power source to enhance the data updating rate. As we all know, the change detection is a prerequisite and base for the electronic navigation data updating. By rapidly developing the area with changes and using the appropriate updating method, we can scientifically maintain the original database of navigation data and terminal physical data. In view of this, starting from application needs for dynamic data updating, this paper analyses change detection methods of navigation data in different versions used for generating incremental data, and focuses on that of rasterizing features and attributes, exploring a new approach to quickly get the incremental data between versions.

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SAR Image Change Detection Method via a Pyramid Pooling Convolutional Neural Network

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324109 ◽

2020 ◽

Author(s):

Rongfang Wang ◽

Fan Ding ◽

Jia-Wei Chen ◽

Bo Liu ◽

Jie Zhang ◽

...

Keyword(s):

Neural Network ◽

Change Detection ◽

Convolutional Neural Network ◽

Detection Method ◽

Sar Image ◽

Image Change Detection

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SAR Image Change Detection Method Based on Neural-CRF Structure

10.1109/igarss47720.2021.9553563 ◽

2021 ◽

Author(s):

Jianlong Zhang ◽

Mengying Cui ◽

Bin Wang

Keyword(s):

Change Detection ◽

Detection Method ◽

Sar Image ◽

Image Change Detection

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Persistent Homology for Land Cover Change Detection

Oxford Research Encyclopedia of Natural Hazard Science ◽

10.1093/acrefore/9780199389407.013.366 ◽

2021 ◽

Author(s):

Djamel Bouchaffra ◽

Faycal Ykhlef

Keyword(s):

Land Cover ◽

Change Detection ◽

Land Cover Change ◽

Vegetation Index ◽

Homology Theory ◽

Urban Morphology ◽

Detection Methods ◽

Landsat Images ◽

Data Repositories ◽

Land Cover Change Detection

The need for environmental protection, monitoring, and security is increasing, and land cover change detection (LCCD) can aid in the valuation of burned areas, the study of shifting cultivation, the monitoring of pollution, the assessment of deforestation, and the analysis of desertification, urban growth, and climate change. Because of the imminent need and the availability of data repositories, numerous mathematical models have been devised for change detection. Given a sample of remotely sensed images from the same region acquired at different dates, the models investigate if a region has undergone change. Even if there is no substantial advantage to using pixel-based classification over object-based classification, a pixel-based change detection approach is often adopted. A pixel can encompass a large region, and it is imperative to determine whether this pixel (input) has changed or not. A changed image is compared to the available ground truth image for pixel-based performance evaluation. Some existing change detection systems do not take into account reversible changes due to seasonal weather effects. In other words, when snow falls in a region, the land cover is not considered as a change because it is seasonal (reversible). Some approaches exploit time series of Landsat images, which are based on the Normalized Difference Vegetation Index technique. Others evaluate built-up expansion to assess urban morphology changes using an unsupervised approach that relies on labels clustering. Change detection methods have also been applied to the field of disaster management using object-oriented image classification. Some methodologies are based on spectral mixture analysis. Other techniques invoke a similarity measure based on the evolution of the local statistics of the image between two dates for vegetation LCCD. Probabilistic approaches based on maximum entropy have been applied to vegetation and forest areas, such as Hustai National Park in Mongolia. Researchers in this field have proposed an LCCD scheme based on a feed-forward neural network using backpropagation for training. This paper invokes the new concept of homology theory, a subfield of algebraic topology. Homology theory is incorporated within a Structural Hidden Markov Model.

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