scholarly journals Assessing new remote sensing aerosol detection algorithms

Eos ◽  
2014 ◽  
Vol 95 (6) ◽  
pp. 60-60
Author(s):  
Colin Schultz
Keyword(s):  
Remote Sensing ◽  
Detection Algorithms

scholarly journals Remote Sensing of Ocean Fronts in Marine Ecology and Fisheries

2021 ◽  
Vol 13 (5) ◽  
pp. 883
Author(s):  
Igor M. Belkin
Keyword(s):  
Remote Sensing ◽  
Marine Ecology ◽  
Popular Front ◽  
Ocean Fronts ◽  
Detection Algorithms ◽  
Concise Review ◽  
Feature Based ◽  
Front Detection

This paper provides a concise review of the remote sensing of ocean fronts in marine ecology and fisheries, with a particular focus on the most popular front detection algorithms and techniques, including those proposed by Canny, Cayula and Cornillon, Miller, Shimada et al., Belkin and O’Reilly, and Nieto et al.. A case is made for a feature-based approach that emphasizes fronts as major structural and circulation features of the ocean realm that play key roles in various aspects of marine ecology.

Comparison of Coastline Extraction Methods and Block Classification Method to Extract Coastlines based on Remote Sensing

2021 ◽  
Author(s):  
Zongmei Li ◽  
Hongmei Chen ◽  
Qin Nie
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Extraction Methods ◽  
Classification Method ◽  
Segmentation Method ◽  
Visual Interpretation ◽  
Threshold Segmentation ◽  
Detection Algorithms ◽  
Block Classification ◽  
Interpretation Method

Abstract Coastlines change with urbanization, and methods to extract coastlines have been previously reported. However, comparisons of these methods are rare. Based on remote sensing image, methods of coastline extraction, namely, the visual interpretation method, the threshold segmentation method, improved normalized water indexes and edge detection algorithms and were studied in Xiamen City, China. The best method to extract coastlines was then determined. The results show that the visual interpretation method for coastline extraction was inefficient. The threshold segmentation method was suitable for small-scale, but not large-scale, coastline extraction, based on coastline area. Improved normalized water indexes were insensitive to sediment shadows. The Sobel method (edge detection algorithms) was suitable for large-scale coastline extraction but could yield false edges. Finally, the block classification method, which combines the advantages of different extraction methods, specifically the threshold segmentation method and improved normalized water indexes, was studied. The results of this study show that coastline extraction by the block classification method is easier and produces better results than coastline extraction by other methods. Therefore, block classification is recommended for the study of coastlines and coastal ecology in large areas.

Floating marine litter detection algorithms and techniques using optical remote sensing data: A review

2021 ◽  
Vol 170 ◽  
pp. 112675
Author(s):  
Konstantinos Topouzelis ◽  
Dimitris Papageorgiou ◽  
Giuseppe Suaria ◽  
Stefano Aliani
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Marine Litter ◽  
Sensing Data ◽  
Detection Algorithms

scholarly journals An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Youkyung Han ◽  
Anjin Chang ◽  
Seokkeun Choi ◽  
Honglyun Park ◽  
Jaewan Choi
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Images ◽  
Sensing Data ◽  
Detection Algorithms ◽  
Change Detection Algorithms ◽  
Spectral Profiles

Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

scholarly journals Alteration Detection of Multispectral/Hyperspectral Images Using Dual-Path Partial Recurrent Networks

2021 ◽  
Vol 13 (23) ◽  
pp. 4802
Author(s):  
Jinlong Li ◽  
Xiaochen Yuan ◽  
Li Feng
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Detection Methods ◽  
Recurrent Networks ◽  
Chi Square ◽  
Detection Model ◽  
Detection Algorithms ◽  
Variant Information ◽  
Intensity Map ◽  
Threshold Algorithms

Numerous alteration detection methods are designed based on image transformation algorithms and divergence of bi-temporal images. In the process of feature transformation, pseudo variant information caused by complex external factors will be highlighted. As a result, the error of divergence between the two images will be further enhanced. In this paper, we propose to fuse the variability of Deep Neural Networks’ (DNNs) structure flexibly with various detection algorithms for bi-temporal multispectral/hyperspectral imagery alteration detection. Specifically, the novel Dual-path Partial Recurrent Networks (D-PRNs) was proposed to project more accurate and effective deep features. The Unsupervised Slow Feature Analysis (USFA), Iteratively Reweighted Multivariate Alteration Detection (IRMAD), and Principal Component Analysis (PCA) were then utilized, respectively, with the proposed D-PRNs, to generate two groups of transformed features corresponding to the bi-temporal remote sensing images. We next employed the Chi-square distance to compute the divergence between two groups of transformed features and, thus, obtain the Alteration Intensity Map. Finally, threshold algorithms K-means and Otsu were, respectively, applied to transform the Alteration Intensity Map into Binary Alteration Map. Experiments were conducted on two bi-temporal remote sensing image datasets, and the testing results proved that the proposed alteration detection model using D-PRNs outperformed the state-of-the-art alteration detection model.

scholarly journals Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2145 ◽  
Author(s):  
Sudhanshu Shekhar Jha ◽  
Rama Rao Nidamanuri
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Satellite Imagery ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Benchmark Dataset ◽  
Remote Sensing Imagery ◽  
Sensing Data ◽  
Detection Algorithms ◽  
Reference Target

Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality of retrievals are functions of various parameters of the sensor, platform, target–background dynamics, targets’ spectral contrast, and atmospheric influence. Generally, target detection in remote sensing imagery has been approached using various statistical detection algorithms with an assumption of linearity in the image formation process. Knowledge on the image acquisition geometry, and spectral features and their stability across different imaging platforms is vital for designing a spectral target detection system. We carried out an integrated target detection experiment for the detection of various artificial target materials. As part of this work, we acquired a benchmark multi-platform hyperspectral and multispectral remote sensing dataset named as ‘Gudalur Spectral Target Detection (GST-D)’ dataset. Positioning artificial targets on different surface backgrounds, we acquired remote sensing data by terrestrial, airborne, and space-borne sensors on 20th March 2018. Various statistical and subspace detection algorithms were applied on the benchmark dataset for the detection of targets, considering the different sources of reference target spectra, background, and the spectral continuity across the platforms. We validated the detection results using the receiver operation curve (ROC) for different cases of detection algorithms and imaging platforms. Results indicate, for some combinations of algorithms and imaging platforms, consistent detection of specific material targets with a detection rate of about 80% at a false alarm rate between 10−2 to 10−3. Target detection in satellite imagery using reference target spectra from airborne hyperspectral imagery match closely with the satellite imagery derived reference spectra. The ground-based in-situ reference spectra offer a quantifiable detection in airborne or satellite imagery. However, ground-based hyperspectral imagery has also provided an equivalent target detection in the airborne and satellite imagery paving the way for rapid acquisition of reference target spectra. The benchmark dataset generated in this work is a valuable resourcefor addressing intriguing questions in target detection using hyperspectral imagery from a realistic landscape perspective.

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