Articoli di riviste sul tema "Imagerie Subsalt"
Segui questo link per vedere altri tipi di pubblicazioni sul tema: Imagerie Subsalt.
Cita una fonte nei formati APA, MLA, Chicago, Harvard e in molti altri stili
Vedi i top-50 articoli di riviste per l'attività di ricerca sul tema "Imagerie Subsalt".
Accanto a ogni fonte nell'elenco di riferimenti c'è un pulsante "Aggiungi alla bibliografia". Premilo e genereremo automaticamente la citazione bibliografica dell'opera scelta nello stile citazionale di cui hai bisogno: APA, MLA, Harvard, Chicago, Vancouver ecc.
Puoi anche scaricare il testo completo della pubblicazione scientifica nel formato .pdf e leggere online l'abstract (il sommario) dell'opera se è presente nei metadati.
Vedi gli articoli di riviste di molte aree scientifiche e compila una bibliografia corretta.
1
Wang, Lin, Hisao-Chi Li, Bai Xue e Chein-I. Chang. "Constrained Band Subset Selection for Hyperspectral Imagery". IEEE Geoscience and Remote Sensing Letters 14, n. 11 (novembre 2017): 2032–36. http://dx.doi.org/10.1109/lgrs.2017.2749209.
2
Wang, Lin, Chein-I. Chang, Li-Chien Lee, Yulei Wang, Bai Xue, Meiping Song, Chuanyan Yu e Sen Li. "Band Subset Selection for Anomaly Detection in Hyperspectral Imagery". IEEE Transactions on Geoscience and Remote Sensing 55, n. 9 (settembre 2017): 4887–98. http://dx.doi.org/10.1109/tgrs.2017.2681278.
3
Zhao, Yong-Qiang, Lei Zhang e Seong G. Kong. "Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification". IEEE Transactions on Geoscience and Remote Sensing 49, n. 2 (febbraio 2011): 747–56. http://dx.doi.org/10.1109/tgrs.2010.2059707.
4
Morenikeji, G. B., O. O. Idowu, B. M. Adeleye, O. R. Bankole e T. W. Anjide. "Effects of Population Increase on Peri-Urban Land Growth in Asa Local Government Area, Kwara State". Environmental Technology and Science Journal 14, n. 1 (1 agosto 2023): 180–88. http://dx.doi.org/10.4314/etsj.v14i1.19.
Abstract (sommario):
The rapid growth of world population and its agglomeration in cities and towns around the world is affecting the longterm outlook for humanity, in such that the process of urban growth and the effect at the peri-urban areas are universal, occurring all over the world. This study aimed at assessing the effects of population increase on the growth of periurban land in Asa local government area, Kwara State. Secondary data, via satellite imageries covering 2000, 2010 and 2021 were mainly used in analyzing the changes that occurred within twenty years. Remote sensing and GIS approaches to satellite imagery processing were adopted using ILWIS and ERDAS IMAGINE 9.2 software to subset the imageries, as well used for classification resampling. The result on built-up area reveals a consistence increase of over 80% between 2000 and 2021. The vegetation cover suffered a serious loss of vegetal land to the tune over 100 hectares due to various degree of development and expansion of the town. The water body also affected with loss in space covered with 80 hectares within the period under study. With a tremendous population increase, the study indicates a high demand for land and vegetal resources, which in turns possess a serious threat to food production. The study concludes that population increase remains the most significant determinant of peri-urban changes with a resultant effect on the wellbeing of peri-urban residents and rural dwellers. It therefore, recommends the adoption and application of strategic physical planning approach in the development of the peri-urban areas.
5
Ye, Bei, Shufang Tian, Qiuming Cheng e Yunzhao Ge. "Application of Lithological Mapping Based on Advanced Hyperspectral Imager (AHSI) Imagery Onboard Gaofen-5 (GF-5) Satellite". Remote Sensing 12, n. 23 (6 dicembre 2020): 3990. http://dx.doi.org/10.3390/rs12233990.
Abstract (sommario):
The Advanced Hyperspectral Imager (AHSI), carried by the Gaofen-5 (GF-5) satellite, is the first hyperspectral sensor that simultaneously offers broad coverage and a broad spectrum. Meanwhile, deep-learning-based approaches are emerging to manage the growing volume of data produced by satellites. However, the application potential of GF-5 AHSI imagery in lithological mapping using deep-learning-based methods is currently unknown. This paper assessed GF-5 AHSI imagery for lithological mapping in comparison with Shortwave Infrared Airborne Spectrographic Imager (SASI) data. A multi-scale 3D deep convolutional neural network (M3D-DCNN), a hybrid spectral CNN (HybridSN), and a spectral–spatial unified network (SSUN) were selected to verify the applicability and stability of deep-learning-based methods through comparison with support vector machine (SVM) based on six datasets constructed by GF-5 AHSI, Sentinel-2A, and SASI imagery. The results show that all methods produce classification results with accuracy greater than 90% on all datasets, and M3D-DCNN is both more accurate and more stable. It can produce especially encouraging results by just using the short-wave infrared wavelength subset (SWIR bands) of GF-5 AHSI data. Accordingly, GF-5 AHSI imagery could provide impressive results and its SWIR bands have a high signal-to-noise ratio (SNR), which meets the requirements of large-scale and large-area lithological mapping. And M3D-DCNN method is recommended for use in lithological mapping based on GF-5 AHSI hyperspectral data.
6
Williams, Sarah E., e Jennifer Cumming. "Measuring Athlete Imagery Ability: The Sport Imagery Ability Questionnaire". Journal of Sport and Exercise Psychology 33, n. 3 (giugno 2011): 416–40. http://dx.doi.org/10.1123/jsep.33.3.416.
Abstract (sommario):
This research aimed to develop and provide initial validation of the Sport Imagery Ability Questionnaire (SIAQ). The SIAQ assesses athletes’ ease of imaging different types of imagery content. Following an extensive pilot study, 375 athletes completed a 20-item SIAQ in Study 1. Exploratory factor analysis revealed a 4-factor model assessing skill, strategy, goal, and affect imagery ability. Confirmatory factor analysis (CFA) established this 4-factor structure in Study 2 (N = 363 athletes). In Study 3 (N = 438 athletes), additional items were added to create a fifth mastery imagery subscale that was confirmed through CFA. Study 4 (N = 220 athletes) compared the SIAQ to the Movement Imagery Questionnaire-3. Significant bivariate correlations (p < .05) confirmed the SIAQ’s concurrent validity but demonstrated differences in imagery ability of different content. Overall, the SIAQ demonstrates good factorial validity, internal and temporal reliability, invariance across gender, and an ability to distinguish among athletes of different competitive levels. Findings highlight the importance of separately assessing imagery ability of different content.
7
Liu, Yufei, Xiaorun Li, Ziqiang Hua e Liaoying Zhao. "EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection". Remote Sensing 13, n. 18 (9 settembre 2021): 3602. http://dx.doi.org/10.3390/rs13183602.
Abstract (sommario):
Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing. However, most of the existing BS methods fail to fully consider the interaction between spectral bands and cannot comprehensively consider the representativeness and redundancy of the selected band subset. To solve these problems, we propose an unsupervised effective band attention reconstruction framework for band selection (EBARec-BS) in this article. The framework utilizes the EBARec network to learn the representativeness of each band to the original band set and measures the redundancy between the bands by calculating the distance of each unselected band to the selected band subset. Subsequently, by designing an adaptive weight to balance the influence of the representativeness metric and redundancy metric on the band evaluation, a final band scoring function is obtained to select a band subset that well represents the original hyperspectral image and has low redundancy. Experiments on three well-known hyperspectral data sets indicate that compared with the existing BS methods, the proposed EBARec-BS is robust to noise bands and can effectively select the band subset with higher classification accuracy and less redundant information.
8
Di, Wei, Quan Pan, Yong-qiang Zhao e Lin He. "Anomaly Target Detection in Hyperspectral Imagery Based on Band Subset Fusion by Fuzzy Integral". Journal of Electronics & Information Technology 30, n. 2 (24 febbraio 2011): 267–71. http://dx.doi.org/10.3724/sp.j.1146.2006.01140.
9
Corlett, John T., John Anton, Steve Kozub e Michel Tardif. "Is Locomotor Distance Estimation Guided by Visual Imagery?" Perceptual and Motor Skills 69, n. 3_suppl (dicembre 1989): 1267–72. http://dx.doi.org/10.2466/pms.1989.69.3f.1267.
Abstract (sommario):
70 subjects were tested for their visual subscale scores on the Movement Imagery Questionnaire and also for their ability to walk, without vision, to a previously viewed target location 9 m away. Imagery ability was hypothesized to correlate with accuracy of “blind” target-directed walking which the literature suggests, without empirical support, is imagery-dependent. No support for this hypothesis was found. Low, medium, and high imagers showed no differences in ability to reproduce target distance accurately or consistently by walking the estimated distance without further visual updating. The results call into question whether task performance is imagery-based or whether subjects use alternative strategies to approach the target.
10
Corlett, John T., John Anton, Steve Kozub e Michel Tardif. "Is Locomotor Distance Estimation Guided by Visual Imagery?" Perceptual and Motor Skills 69, n. 3-2 (dicembre 1989): 1267–72. http://dx.doi.org/10.1177/00315125890693-237.
Abstract (sommario):
70 subjects were tested for their visual subscale scores on the Movement Imagery Questionnaire and also for their ability to walk, without vision, to a previously viewed target location 9 m away. Imagery ability was hypothesized to correlate with accuracy of “blind” target-directed walking which the literature suggests, without empirical support, is imagery-dependent. No support for this hypothesis was found. Low, medium, and high imagers showed no differences in ability to reproduce target distance accurately or consistently by walking the estimated distance without further visual updating. The results call into question whether task performance is imagery-based or whether subjects use alternative strategies to approach the target.
11
Adam, Fathalrahman, Thomas Esch e Mihai Datcu. "Feature Investigation for Large Scale Urban Detection Using Landsat Imagery". Proceedings 2, n. 7 (22 marzo 2018): 349. http://dx.doi.org/10.3390/ecrs-2-05162.
Abstract (sommario):
Many works dealing with the problem of urban detection at large scale have been published, but very little attention has been paid to the investigation of the features’ relative importance. Feature selection is known to be an NP-hard problem, which means it can not be solved in polynomial time, but there are many heuristics suggested to approximate the solution. In this paper, a survey of the features used for large scale urban detection is presented, then the question of finding the best subset of features is investigated. Using Landsat scenes of five urban areas, most common features were extracted to represent the full feature set. Employing mutual information based ranking methods, Support Vector Machine (SVM) and Random Forest feature ranking, an importance score was assigned to each feature by each method. To aggregate the individual rankings of features, a two stage voting scheme was implemented to choose a subset of size N as the most relevant features. The most important features for all five cities taken together were listed.
12
Baig, Muhammad Zeeshan, Nauman Aslam, Hubert P. H. Shum e Li Zhang. "Differential evolution algorithm as a tool for optimal feature subset selection in motor imagery EEG". Expert Systems with Applications 90 (dicembre 2017): 184–95. http://dx.doi.org/10.1016/j.eswa.2017.07.033.
13
Wang, Jie, Zuren Feng, Xiaodong Ren, Na Lu, Jing Luo e Lei Sun. "Feature subset and time segment selection for the classification of EEG data based motor imagery". Biomedical Signal Processing and Control 61 (agosto 2020): 102026. http://dx.doi.org/10.1016/j.bspc.2020.102026.
14
Amorim, André, Bruno Travassos e Pedro Mendes. "Imagery ability in Boccia: Comparison among federate athletes from different medical sport groups". Motricidade 13, n. 4 (27 gennaio 2018): 46. http://dx.doi.org/10.6063/motricidade.11780.
Abstract (sommario):
The aim of this study was to analyse and compare movement visualization ability in federate and non-federate Boccia athletes, and among federate Boccia medical sport groups. Forty-two Boccia athletes (Federate N = 24; Non-federate N = 18) at an average age of 35.8 (SD = 11.19) participated in this study. The Portuguese version of Movement Imagery Questionnaire - 3 (MIQ-3), was used for this study. The participants were evaluated on the internal and external visual imagery. Statistics was carried out following the method of interference based on the magnitude of the effects. Results showed a great effect of expertise in imagery ability. The comparison between federate and non-federate Boccia athletes showed a great effect in the Internal Visual subscale and a moderate effect in the External Visual subscale. It was also observed differences between athletes from different medical-sports groups, revealing that the requirements of the sport linked to their action abilities provides them with different Imagery abilities. These results clearly influence the prescription of imagery training programs for different groups taking into account different medical-practice groups.
15
Hewahi, Nabil M., e Eyad A. Alashqar. "Wrapper Feature Selection based on Genetic Algorithm for Recognizing Objects from Satellite Imagery". Journal of Information Technology Research 8, n. 3 (luglio 2015): 1–20. http://dx.doi.org/10.4018/jitr.2015070101.
Abstract (sommario):
Object recognition is a research area that aims to associate objects to categories or classes. The recognition of object specific geospatial features, such as roads, buildings and rivers, from high-resolution satellite imagery is a time consuming and expensive problem in the maintenance cycle of a Geographic Information System (GIS). Feature selection is the task of selecting a small subset from original features that can achieve maximum classification accuracy and reduce data dimensionality. This subset of features has some very important benefits like, it reduces computational complexity of learning algorithms, saves time, improve accuracy and the selected features can be insightful for the people involved in problem domain. This makes feature selection as an indispensable task in classification task. In this work, the authors propose a new approach that combines Genetic Algorithms (GA) with Correlation Ranking Filter (CRF) wrapper to eliminate unimportant features and obtain better features set that can show better results with various classifiers such as Neural Networks (NN), K-nearest neighbor (KNN), and Decision trees. The approach is based on GA as an optimization algorithm to search the space of all possible subsets related to object geospatial features set for the purpose of recognition. GA is wrapped with three different classifier algorithms namely neural network, k-nearest neighbor and decision tree J48 as subset evaluating mechanism. The GA-ANN, GA-KNN and GA-J48 methods are implemented using the WEKA software on dataset that contains 38 extracted features from satellite images using ENVI software. The proposed wrapper approach incorporated the Correlation Ranking Filter (CRF) for spatial features to remove unimportant features. Results suggest that GA based neural classifiers and using CRF for spatial features are robust and effective in finding optimal subsets of features from large data sets.
16
Anand, Raju, Sathishkumar Samiaappan, Shanmugham Veni, Ethan Worch e Meilun Zhou. "Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization". Journal of Imaging 8, n. 5 (27 aprile 2022): 126. http://dx.doi.org/10.3390/jimaging8050126.
Abstract (sommario):
In this research, we study a new metaheuristic algorithm called Moth–Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statistical classifier is high. Thus, the problem is to select a subset of bands without compromising the classification accuracy. One of the ways to solve this problem is to model an objective function that measures class separability and utilize it to arrive at a subset of bands. In this research, we studied MFO to select optimal spectral bands for classification. MFO is inspired by the behavior of moths with respect to flames, which is the navigation method of moths in nature called transverse orientation. In MFO, a moth navigates the search space through a process called transverse orientation by keeping a constant angle with the Moon, which is a compelling strategy for traveling long distances in a straight line, considering that the Moon’s distance from the moth is considerably long. Our research tested MFO on three benchmark hyperspectral datasets—Indian Pines, University of Pavia, and Salinas. MFO produced an Overall Accuracy (OA) of 88.98%, 94.85%, and 97.17%, respectively, on the three datasets. Our experimental results indicate that MFO produces better OA and Kappa when compared to state-of-the-art band selection algorithms such as particle swarm optimization, grey wolf, cuckoo search, and genetic algorithms. The analysis results prove that the proposed approach effectively addresses the spectral band selection problem and provides a high classification accuracy.
17
Samadzadegan, Farhad, e Hadiseh Hasani. "Determination Optimum SVMs Classifiers for Hyperspectral Imagery Based on Ant Colony Optimization". Key Engineering Materials 500 (gennaio 2012): 792–98. http://dx.doi.org/10.4028/www.scientific.net/kem.500.792.
Abstract (sommario):
Referring to robustness of SVMs in high dimensional space, they are reliable tools for classification of hyperspectral imagery. However their performance is directly affected by two aspects: parameter determination and optimum feature subset selection. According to capacity of population based meta-heuristic optimization algorithm such as Ant Colony Optimization (ACO), they can find optimum or near optimum solution in complex optimization problems. This paper evaluates the potential of Binary ACO (BACO) in parameter determination, feature selection and both of them simultaneously in SVMs based classification system for hyperspectral imagery. Obtained results in comparison with genetic algorithm show superiority of BACO.
18
Suárez Rozo, Manuel Enrique, Sara Trapero-Asenjo, Daniel Pecos-Martín, Samuel Fernández-Carnero, Tomás Gallego-Izquierdo, José Jesús Jiménez Rejano e Susana Nunez-Nagy. "Reliability of the Spanish Version of the Movement Imagery Questionnaire-3 (MIQ-3) and Characteristics of Motor Imagery in Institutionalized Elderly People". Journal of Clinical Medicine 11, n. 20 (14 ottobre 2022): 6076. http://dx.doi.org/10.3390/jcm11206076.
Abstract (sommario):
Motor imagery (MI) training is increasingly used to improve the performance of specific motor skills. The Movement Imagery Questionnaire-3 (MIQ-3) is an instrument for assessing MI ability validated in Spanish although its reliability has not yet been studied in the elderly population. The main objective of this study was to test its reliability in institutionalized elderly people. Secondarily, we studied whether there are differences according to gender and age in MI ability (measured by the MIQ-3) and in temporal congruency (measured by mental chronometry of elbow and knee flexion-extension and getting up and sitting down from chair movements). The subjects were 60 elderly, institutionalized, Spanish-speaking individuals without cognitive impairment or dementia, and aged between 70 and 100 years. Cronbach’s alpha showed high internal consistency in the internal visual and external visual subscales and moderate in the kinesthetic subscale. The intraclass correlation coefficient showed good test-retest reliability for all three subscales. Mixed factorial analysis of variances (ANOVAs) showed that MI ability decreased with increasing age range, the imagery time decreased concerning the execution of the same movement, and there were no gender differences in either IM ability or temporal congruence. The Spanish version of the MIQ-3 is a reliable instrument for measuring MI ability in institutionalized elderly.
19
Samadzadega, F., e H. Hasani. "DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (11 dicembre 2015): 651–56. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-651-2015.
Abstract (sommario):
Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.
20
Hatakeyama, Takao. "Associations between Autistic-like Traits and Imagery Ability". Vision 8, n. 1 (12 marzo 2024): 13. http://dx.doi.org/10.3390/vision8010013.
Abstract (sommario):
This article examines empirical associations between qualities of the imagination, mental imagery, ands cognitive abilities with special reference to autism. This study is the first to explore the empirical relationships between autistic-like traits and tests of imagery differences. Imaginative impairments and distinctive sensory characteristics in individuals with autism spectrum disorder (ASD) should be reflected in their interactions with mental imagery. However, the relationship between ASD and imaging traits remains unclear. Based on the hypothesis that the degree of autistic-like traits is reflected in imagery traits, this study examined how the individual Autism Spectrum Quotient (AQ) relates to imagery ability in 250 college students. Two vividness tests and one imagery-type test were used to assess imagery ability. Scores in each imagery test were compared between the high-scoring group classified by the AQ and the rest of the participants and between the low-scoring group classified by the AQ and the other participants. This study also directly compared imagery test scores between the high- and low-scoring groups. In terms of the total AQ score, the high-scoring group exhibited lower visualization scores. Regarding AQ subscales, “imagination” had the most extensive relationship with imagery traits, with the high-scoring group (unimaginative) showing lower imagery vividness across various modalities as well as lower visualization and verbalization scores. This was followed by the “attention to detail” subscale, on which the high-scoring group (attentive to detail) showed higher vividness of visual imagery. The results of the low-scoring group exhibited, on the whole, opposite imagery tendencies to the high-scoring group. The results indicate that autistic-like traits are associated with qualities of the imagination and especially mental imagery ability.
21
Yang, Lingbo, Lamin Mansaray, Jingfeng Huang e Limin Wang. "Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery". Remote Sensing 11, n. 5 (3 marzo 2019): 514. http://dx.doi.org/10.3390/rs11050514.
Abstract (sommario):
Geographic object-based image analysis (GEOBIA) has been widely used in the remote sensing of agricultural crops. However, issues related to image segmentation, data redundancy and performance of different classification algorithms with GEOBIA have not been properly addressed in previous studies, thereby compromising the accuracy of subsequent thematic products. It is in this regard that the current study investigates the optimal scale parameter (SP) in multi-resolution segmentation, feature subset, and classification algorithm for use in GEOBIA based on multisource satellite imagery. For this purpose, a novel supervised optimal SP selection method was proposed based on information gain ratio, and was then compared with a preexisting unsupervised optimal SP selection method. Additionally, the recursive feature elimination (RFE) and enhanced RFE (EnRFE) algorithms were modified to generate an improved EnRFE (iEnRFE) algorithm, which was then compared with its precursors in the selection of optimal classification features. Based on the above, random forest (RF), gradient boosting decision tree (GBDT) and support vector machine (SVM) were applied to segmented objects for crop classification. The results indicated that the supervised optimal SP selection method is more suitable for application in heterogeneous land cover, whereas the unsupervised method proved more efficient as it does not require reference segmentation objects. The proposed iEnRFE method outperformed the preexisting EnRFE and RFE methods in optimal feature subset selection as it recorded the highest accuracy and less processing time. The RF, GBDT, and SVM algorithms achieved overall classification accuracies of 91.8%, 92.4%, and 90.5%, respectively. GBDT and RF recorded higher classification accuracies and utilized much less computational time than SVM and are, therefore, considered more suitable for crop classification requiring large numbers of image features. These results have shown that the proposed object-based crop classification scheme could provide a valuable reference for relevant applications of GEOBIA in crop recognition using multisource satellite imagery.
22
Chang, Hongli, e Jimin Yang. "Automated Selection of a Channel Subset Based on the Genetic Algorithm in a Motor Imagery Brain-Computer Interface System". IEEE Access 7 (2019): 154180–91. http://dx.doi.org/10.1109/access.2019.2944938.
23
Probeck, Markus, Ralf Ludwig e Wolfram Mauser. "Fusion of NOAA-AVHRR imagery and geographical information system techniques to derive subscale land cover information for the upper Danube watershed". Hydrological Processes 19, n. 12 (15 agosto 2005): 2407–18. http://dx.doi.org/10.1002/hyp.5892.
24
Wang, Yulei, Lin Wang, Hongye Xie e Chein-I. Chang. "Fusion of Various Band Selection Methods for Hyperspectral Imagery". Remote Sensing 11, n. 18 (12 settembre 2019): 2125. http://dx.doi.org/10.3390/rs11182125.
Abstract (sommario):
This paper presents an approach to band selection fusion (BSF) which fuses bands produced by a set of different band selection (BS) methods for a given number of bands to be selected, nBS. Since each BS method has its own merit in finding the desired bands, various BS methods produce different band subsets with the same nBS. In order to take advantage of these different band subsets, the proposed BSF is performed by first finding the union of all band subsets produced by a set of BS methods as a joint band subset (JBS). Due to the fact that a band selected by one BS method in JBS may be also selected by other BS methods, in this case each band in JBS is prioritized by the frequency of the band appearing in the band subsets to be fused. Such frequency is then used to calculate the priority probability of this particular band in the JBS. Because the JBS is obtained by taking the union of all band subsets, the number of bands in the JBS is at least equal to or greater than nBS. So, there may be more than nBS bands, in which case, BSF uses the frequency-calculated priority probabilities to select nBS bands from JBS. Two versions of BSF, called progressive BSF and simultaneous BSF, are developed for this purpose. Of particular interest is that BSF can prioritize bands without band de-correlation, which has been a major issue in many BS methods using band prioritization as a criterion to select bands.
25
Hoffman, Jay, Steven Ackerman, Yinghui Liu e Jeffrey Key. "The Detection and Characterization of Arctic Sea Ice Leads with Satellite Imagers". Remote Sensing 11, n. 5 (4 marzo 2019): 521. http://dx.doi.org/10.3390/rs11050521.
Abstract (sommario):
Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will likely result in changes in lead distributions, so monitoring their characteristics is increasingly important. Here we present a methodology to detect and characterize sea ice leads using satellite imager thermal infrared window channels. A thermal contrast method is first used to identify possible sea ice lead pixels, then a number of geometric and image analysis tests are applied to build a subset of positively identified leads. Finally, characteristics such as width, length and orientation are derived. This methodology is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) observations for the months of January through April over the period of 2003 to 2018. The algorithm results are compared to other satellite estimates of lead distribution. Lead coverage maps and statistics over the Arctic illustrate spatial and temporal lead patterns.
26
Kiala, Zolo, Onisimo Mutanga, John Odindi e Kabir Peerbhay. "Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed". Remote Sensing 11, n. 16 (13 agosto 2019): 1892. http://dx.doi.org/10.3390/rs11161892.
Abstract (sommario):
In the recent past, the volume of spatial datasets has significantly increased. This is attributed to, among other factors, higher sensor temporal resolutions of the recently launched satellites. The increased data, combined with the computation and possible derivation of a large number of indices, may lead to high multi-collinearity and redundant features that compromise the performance of classifiers. Using dimension reduction algorithms, a subset of these features can be selected, hence increasing their predictive potential. In this regard, an investigation into the application of feature selection techniques on multi-temporal multispectral datasets such as Sentinel-2 is valuable in vegetation mapping. In this study, ten feature selection methods belonging to five groups (Similarity-based, statistical-based, Sparse learning based, Information theoretical based, and wrappers methods) were compared based on f-score and data size for mapping a landscape infested by the Parthenium weed (Parthenium hysterophorus). Overall, results showed that ReliefF (a Similarity-based approach) was the best performing feature selection method as demonstrated by the high f-score values of Parthenium weed and a small size of optimal features selected. Although svm-b (a wrapper method) yielded the highest accuracies, the size of optimal subset of selected features was quite large. Results also showed that data size affects the performance of feature selection algorithms, except for statistically-based methods such as Gini-index and F-score and svm-b. Findings in this study provide a guidance on the application of feature selection methods for accurate mapping of invasive plant species in general and Parthenium weed, in particular, using new multispectral imagery with high temporal resolution.
27
Safarov, Furkat, Kuchkorov Temurbek, Djumanov Jamoljon, Ochilov Temur, Jean Chamberlain Chedjou, Akmalbek Bobomirzaevich Abdusalomov e Young-Im Cho. "Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture". Sensors 22, n. 24 (13 dicembre 2022): 9784. http://dx.doi.org/10.3390/s22249784.
Abstract (sommario):
Currently, there is a growing population around the world, and this is particularly true in developing countries, where food security is becoming a major problem. Therefore, agricultural land monitoring, land use classification and analysis, and achieving high yields through efficient land use are important research topics in precision agriculture. Deep learning-based algorithms for the classification of satellite images provide more reliable and accurate results than traditional classification algorithms. In this study, we propose a transfer learning based residual UNet architecture (TL-ResUNet) model, which is a semantic segmentation deep neural network model of land cover classification and segmentation using satellite images. The proposed model combines the strengths of residual network, transfer learning, and UNet architecture. We tested the model on public datasets such as DeepGlobe, and the results showed that our proposed model outperforms the classic models initiated with random weights and pre-trained ImageNet coefficients. The TL-ResUNet model outperforms other models on several metrics commonly used as accuracy and performance measures for semantic segmentation tasks. Particularly, we obtained an IoU score of 0.81 on the validation subset of the DeepGlobe dataset for the TL-ResUNet model.
28
Kheira, Djelloul, e M. Beladgham. "Performance of channel selection used for Multi-class EEG signal classification of motor imagery". Indonesian Journal of Electrical Engineering and Computer Science 15, n. 3 (1 settembre 2019): 1305. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1305-1312.
Abstract (sommario):
<p>In this paper, a study of a non-invasive brain-machine interfaces for the classification of 4 imaginary are presented. Performance comparisons using time-frequency analysis between the Linear Discriminant Analysis motor activities (left hand, right hand, foot, tongue) with the BCI competition III dataset IIIa is (LDA), the Support Vector Machine (SVM) and the K-Nearest Neighbors (KNN) algorithms have been carried. The number and position of electrodes for each subject were investigated to provide an improvement for the classification accuracy of the algorithm. Results show that the electrode positions varied from subject to subject; moreover , using one subset of the channels enhanced the classification performances compared to literature data. an average accuracy of 86.06% was observed among all 3 subjects.<strong></strong></p>
29
Monsma, Eva V., e Lynnette Y. Overby. "The Relationship between Imagery and Competitive Anxiety in Ballet Auditions". Journal of Dance Medicine & Science 8, n. 1 (marzo 2004): 11–18. http://dx.doi.org/10.1177/1089313x0400800102.
Abstract (sommario):
Aligned with competitive anxiety research in athletics, this study explored audition anxiety and the role of imagery in the anxiety-performance relationship among 131 female auditioning ballet dancers. A better understanding of how auditioning dancers experience anxiety and associated image content can help train dancers preparing for anxiety-provoking, high-stakes performances. The CSAI-2 assessing competitive state anxiety and confidence and the SIQ assessing the cognitive and motivational functions of imagery were slightly modified for administration in the audition context. The MIQ-R was used to assess movement imagery. All instrument subscales, with the exception of the CG-Strategies subscale of the SIQ, demonstrated adequate internal consistency. Ballet dancers’ scores were similar to those reported by aesthetic sport athletes. Obtaining a position with a dance company was used as a proxy for defining success. Successful dancers with prior audition success were more confident than those without prior success and unsuccessful dancers with, and without, prior success. As a group, successful dancers experienced less cognitive anxiety and more somatic anxiety than unsuccessful dancers. Although imagery ability and image content did not differentiate dancers by performance, confident dancers had higher kinesthetic imagery ability and used more mastery and less arousal imagery than less confident dancers. In contrast, cognitively and somatically anxious dancers used less mastery and more arousal imagery. The athletic paradigm appears to be an appropriate framework for studying performance-related anxiety among dancers. Dancers and practitioners are encouraged to focus on mastery images for increasing confidence and decreasing anxiety. Dancers with prior audition success may be incorporating theses experiences in generating arousal imagery shown to predicted somatic anxiety, anxiety that does not appear to be detrimental to performance when cognitive anxiety is controlled.
30
Yassine, H., K. Tout e M. Jaber. "IMPROVING LULC CLASSIFICATION FROM SATELLITE IMAGERY USING DEEP LEARNING – EUROSAT DATASET". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (28 giugno 2021): 369–76. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-369-2021.
Abstract (sommario):
Abstract. Machine learning (ML) has proven useful for a very large number of applications in several domains. It has realized a remarkable growth in remote-sensing image analysis over the past few years. Deep Learning (DL) a subset of machine learning were applied in this work to achieve a better classification of Land Use Land Cover (LULC) in satellite imagery using Convolutional Neural Networks (CNNs). EuroSAT benchmarking data set is used as training data set which uses Sentinel-2 satellite images. Sentinel-2 provides images with 13 spectral feature bands, but surprisingly little attention has been paid to these features in deep learning models. The majority of applications focused only on using RGB due to high availability of the RGB models in computer vision. While RGB gives an accuracy of 96.83% using CNN, we are presenting two approaches to improve the classification performance of Sentinel-2 images. In the first approach, features are extracted from 13 spectral feature bands of Sentinel-2 instead of RGB which leads to accuracy of 98.78%. In the second approach features are extracted from 13 spectral bands of Sentinel-2 in addition to calculated indices used in LULC like Blue Ratio (BR), Vegetation index based on Red Edge (VIRE) and Normalized Near Infrared (NNIR), etc. which gives a better accuracy of 99.58%.
31
Abadal, Saüc, Luis Salgueiro, Javier Marcello e Verónica Vilaplana. "A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery". Remote Sensing 13, n. 22 (12 novembre 2021): 4547. http://dx.doi.org/10.3390/rs13224547.
Abstract (sommario):
There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach. Our proposal is a dual-network model with two branches: the Single Image Super-Resolution branch, that reconstructs a high-resolution version of the input image, and the Semantic Segmentation Super-Resolution branch, that predicts a high-resolution segmentation map with a scaling factor of 2. We performed several experiments to find the best architecture, training and testing on a subset of the S2GLC 2017 dataset. We based our model on the DeepLabV3+ architecture, enhancing the model and achieving an improvement of 5% on IoU and almost 10% on the recall score. Furthermore, our qualitative results demonstrate the effectiveness and usefulness of the proposed approach.
32
Hari Krishna, D., I. A.Pasha e T. Satya Savithri. "Multiclass classification of motor imagery EEG signals using ensemble classifiers & cross-correlation". International Journal of Engineering & Technology 7, n. 2.6 (11 marzo 2018): 163. http://dx.doi.org/10.14419/ijet.v7i2.6.10144.
Abstract (sommario):
To communicate without any muscle movement and purely based on brain signal has been the goal of Brain computer interfacing (BCI). Recent BCI based studies reported more and more accurate detection of brain states. This paper proposes a study that detects EEG signal belonging todifferent imaginary motor activities (Right leg, right hand, left leg and left hand). The Electroencephalogram (EEG) signal has been conditioned by band pass filter (BPF) to improve signal to noise ratio (SNR). The proposed method is based on similarity between signals to extract features. For measuring the similarity between signals, Cross correlation (CC) is used. An ensemble set of five classifiers (Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB) and Binary Decision Tree) was used collectively. As the similarity measurement was binary in nature, one versus rest (OVR) approach was used for multi class classification. Random subset of features was used to train the ensemble of classifiers. The classification label was obtained by using majority voting. An average accuracy of 89.57% was observed among all 10 subjects.
33
Akay, A. E., B. Gencal e İ. Taş. "SPATIOTEMPORAL CHANGE DETECTION USING LANDSAT IMAGERY: THE CASE STUDY OF KARACABEY FLOODED FOREST, BURSA, TURKEY". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (13 novembre 2017): 31–35. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-31-2017.
Abstract (sommario):
This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.
34
Meyer, J., D. Rettenmund e S. Nebiker. "LONG-TERM VISUAL LOCALIZATION IN LARGE SCALE URBAN ENVIRONMENTS EXPLOITING STREET LEVEL IMAGERY". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (3 agosto 2020): 57–63. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-57-2020.
Abstract (sommario):
Abstract. In this paper, we present our approach for robust long-term visual localization in large scale urban environments exploiting street level imagery. Our approach consists of a 2D-image based localization using image retrieval (NetVLAD) to select reference images. This is followed by a 3D-structure based localization with a robust image matcher (DenseSfM) for accurate pose estimation. This visual localization approach is evaluated by means of the ‘Sun’ subset of the RobotCar seasons dataset, which is part of the Visual Localization benchmark. As the results on the RobotCar benchmark dataset are nearly on par with the top ranked approaches, we focused our investigations on reproducibility and performance with own data. For this purpose, we created a dataset with street-level imagery. In order to have independent reference and query images, we used a road-based and a tram-based mapping campaign with a time difference of four years. The approximately 90% successfully oriented images of both datasets are a good indicator for the robustness of our approach. With about 50% success rate, every second image could be localized with a position accuracy better than 0.25 m and a rotation accuracy better than 2°.
35
Chen, Buo-Fu, Boyo Chen, Hsuan-Tien Lin e Russell L. Elsberry. "Estimating Tropical Cyclone Intensity by Satellite Imagery Utilizing Convolutional Neural Networks". Weather and Forecasting 34, n. 2 (1 aprile 2019): 447–65. http://dx.doi.org/10.1175/waf-d-18-0136.1.
Abstract (sommario):
Abstract Accurately estimating tropical cyclone (TC) intensity is one of the most critical steps in TC forecasting and disaster warning/management. For over 40 years, the Dvorak technique (and several improved versions) has been applied for estimating TC intensity by forecasters worldwide. However, the operational Dvorak techniques primarily used in various agencies have several deficiencies, such as inherent subjectivity leading to inconsistent intensity estimates within various basins. This collaborative study between meteorologists and data scientists has developed a deep-learning model using satellite imagery to estimate TC intensity. The conventional convolutional neural network (CNN), which is a mature technology for object classification, requires several modifications when being used for directly estimating TC intensity (a regression task). Compared to the Dvorak technique, the CNN model proposed here is objective and consistent among various basins; it has been trained with satellite infrared brightness temperature and microwave rain-rate data from 1097 global TCs during 2003–14 and optimized with data from 188 TCs during 2015–16. This paper also introduces an upgraded version that further improves the accuracy by using additional TC information (i.e., basin, day of year, local time, longitude, and latitude) and applying a postsmoothing procedure. An independent testing dataset of 94 global TCs during 2017 has been used to evaluate the model performance. A root-mean-square intensity difference of 8.39 kt (1 kt ≈ 0.51 m s−1) is achieved relative to the best track intensities. For a subset of 482 samples analyzed with reconnaissance observations, a root-mean-square intensity difference of 8.79 kt is achieved.
36
Athanasiou, Alkinoos, Nikos Terzopoulos, Niki Pandria, Ioannis Xygonakis, Nicolas Foroglou, Konstantinos Polyzoidis e Panagiotis D. Bamidis. "Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury". Neural Plasticity 2018 (2018): 1–20. http://dx.doi.org/10.1155/2018/9354207.
Abstract (sommario):
Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.
37
Duarte-Carvajalino, Julio Martin, Elías Alexander Silva-Arero, Gerardo Antonio Góez-Vinasco, Laura Marcela Torres-Delgado, Oscar Dubán Ocampo-Paez e Angela María Castaño-Marín. "Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms". Horticulturae 7, n. 7 (2 luglio 2021): 176. http://dx.doi.org/10.3390/horticulturae7070176.
Abstract (sommario):
This work presents quantitative detection of water stress and estimation of the water stress level: none, light, moderate, and severe on potato crops. We use hyperspectral imagery and state of the art machine learning algorithms: random decision forest, multilayer perceptron, convolutional neural networks, support vector machines, extreme gradient boost, and AdaBoost. The detection and estimation of water stress in potato crops is carried out on two different phenological stages of the plants: tubers differentiation and maximum tuberization. The machine learning algorithms are trained with a small subset of each hyperspectral image corresponding to the plant canopy. The results are improved using majority voting to classify all the canopy pixels in the hyperspectral images. The results indicate that both detection of water stress and estimation of the level of water stress can be obtained with good accuracy, improved further by majority voting. The importance of each band of the hyperspectral images in the classification of the images is assessed by random forest and extreme gradient boost, which are the machine learning algorithms that perform best overall on both phenological stages and detection and estimation of water stress in potato crops.
38
Angel, Yoseline, Rasmus Houborg e Matthew F. McCabe. "Reconstructing Cloud Contaminated Pixels Using Spatiotemporal Covariance Functions and Multitemporal Hyperspectral Imagery". Remote Sensing 11, n. 10 (14 maggio 2019): 1145. http://dx.doi.org/10.3390/rs11101145.
Abstract (sommario):
One of the major challenges in optical-based remote sensing is the presence of clouds, which imposes a hard constraint on the use of multispectral or hyperspectral satellite imagery for earth observation. While some studies have used interpolation models to remove cloud affected data, relatively few aim at restoration via the use of multi-temporal reference images. This paper proposes not only the use of image time-series, but also the implementation of a geostatistical model that considers the spatiotemporal correlation between them to fill the cloud-related gaps. Using Hyperion hyperspectral images, we demonstrate a capacity to reconstruct cloud-affected pixels and predict their underlying surface reflectance values. To do this, cloudy pixels were masked and a parametric family of non-separable covariance functions was automated fitted, using a composite likelihood estimator. A subset of cloud-free pixels per scene was used to perform a kriging interpolation and to predict the spectral reflectance per each cloud-affected pixel. The approach was evaluated using a benchmark dataset of cloud-free pixels, with a synthetic cloud superimposed upon these data. An overall root mean square error (RMSE) of between 0.5% and 16% of the reflectance was achieved, representing a relative root mean square error (rRMSE) of between 0.2% and 7.5%. The spectral similarity between the predicted and reference reflectance signatures was described by a mean spectral angle (MSA) of between 1° and 11°, demonstrating the spatial and spectral coherence of predictions. The approach provides an efficient spatiotemporal interpolation framework for cloud removal, gap-filling, and denoising in remotely sensed datasets.
39
Lewandowski, Wendy, Ann Jacobson, Patrick A. Palmieri, Thomas Alexander e Richard Zeller. "Biological Mechanisms Related to the Effectiveness of Guided Imagery for Chronic Pain". Biological Research For Nursing 13, n. 4 (26 novembre 2010): 364–75. http://dx.doi.org/10.1177/1099800410386475.
Abstract (sommario):
Specific aims of this pilot study were to (a) determine the effect of a guided imagery (GI) intervention over an 8-week period on pain and pain disability in a sample of persons with chronic noncancer pain (CNCP) and (b) analyze the mediating effects of neuroendocrine and neuroimmune functioning on the effectiveness of GI on outcome variables. A simple interrupted time-series design (12-week period) was used. GI was introduced at Week 4 and used daily by 25 participants for the remaining 8 weeks. Measures of pain and pain disability were obtained at the beginning of the study period and at six repeated 2-week intervals. Measures of hypothalamic-pituitary-adrenal (HPA) axis activation (plasma cortisol), immune-mediated analgesia (lymphocyte subset counts and proliferation), and immune-mediated hyperalgesia (interleukin-1β) were obtained at the beginning of the study and at Week 11. Usual pain levels were lower after the introduction of GI at Week 4 (Wilks' λ = 52.31; df = 2, 22; p = .000). Pain disability levels were lower after the introduction of GI at Week 4 (Wilks' λ = 5.98; df = 6, 18; p = .001). Correlation coefficients between change scores of dependent variables and mediating variables were not significant. GI was effective in reducing pain intensity and pain disability over an 8-week period; however, the results did not support the expected effects of decreased HPA axis activation, improved immune-mediated analgesia, and reduced immune-mediated hyperalgesia in mediating these outcomes. These findings may be related to procedural and theoretical issues and limitations related to the study design.
40
Astiti, Sagung Putri Chandra, Takahiro Osawa e I. Wayan Nuarsa. "IDENTIFICATION OF SHORELINE CHANGES USING SENTINEL 2 IMAGERY DATA IN CANGGU COASTAL AREA". ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) 13, n. 2 (30 novembre 2019): 191. http://dx.doi.org/10.24843/ejes.2019.v13.i02.p07.
Abstract (sommario):
Coastal areas in the Canggu and Seminyak areas located in Badung Regency, Bali Province are very attractive tourism. The development of tourism has an impact on coastal conditions. The coastal conditions analyzed are changes in coastline that occurred during 2015-2019 using remote sensing. The satellite image data used in the analysis is Sentinel 2A image data that can be accessed for free with a spatial resolution of 10 meters. Image data processing is divided into three stages, namely preprocessing, processing, and post processing using Sentinel Application Platform (SNAP) software. The preprocessing stage includes the resampling, masking, and subset areas. The processing stage includes digitizing the coastal area, digitizing accuracy analysis using the Support Vector Machine (SVM) method, and the post processing stage including correction of shoreline changes. Bands in image data used for detection of coastal areas are band 8 (NIR), 8A (narrow NIR), 11 (SWIR), and 12 (SWIR). Based on the results of the analysis of shoreline changes carried out during 2015-2019, it was found that the average shoreline changes were 1.42 m / year with erosion conditions in which the dominant wind direction originated from the southwest towards the northeast coast of the sea of ??Bali. The results of digitizing the coastal area using the Fine Gaussian SVM method with the greatest accuracy value is 87.8%.
Keywords: Shoreline Change, Remote Sensing, Sentinel 2A, SVM, Wind Direction
41
Moradi, F., A. Zarei, S. Ranjbar e S. Homayouni. "WHEAT BIOMASS ESTIMATION FROM UAV IMAGERY USING AN ENSEMBLE LEARNING APPROACH WITH BAYESIAN OPTIMIZATION". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (14 gennaio 2023): 515–22. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-515-2023.
Abstract (sommario):
Abstract. Wheat is one of the most important food supply and food security globally, especially in developing countries. Therefore, predicting the performance and determining the factors that affect the production of this product is very important. Biomass is one of the crop’s most important biophysical parameters, and its correct estimation can help improve accurate monitoring of growth and crop performance forecasting. With the recent advances in remote sensing, access to aerial images taken by unmanned aerial vehicles (UAV) for monitoring crops has been provided. This study investigates the potential of visible UAV images and the resulting vegetation indices to estimate the dry biomass of two types of Brazilian wheat. For this purpose, the performance of three regression algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM), to estimate wheat biomass was evaluated. Also, to improve the performance of regression models, Bayesian optimization (BO) was used to adjust the Hyper-parameters, and random forest feature selection was used to select the optimal subset of features. Based on the results, the XGB algorithm with the Root Mean Square Error (RMSE) of about 911.86 (Kg/ha) and coefficient of determination (R2) of about 0.89% showed better performance in biomass estimation than other algorithms.
42
Dobrinić, D., M. Gašparović e D. Medak. "EVALUATION OF FEATURE SELECTION METHODS FOR VEGETATION MAPPING USING MULTITEMPORAL SENTINEL IMAGERY". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (30 maggio 2022): 485–91. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-485-2022.
Abstract (sommario):
Abstract. With the recent advances in remote sensing technologies for Earth observation (EO), many different remote sensors (e.g., optical, radar) collect data with distinctive properties. EO data have been employed to monitor croplands and forested areas, oceans and seas, urban settlements, and natural hazards. The spectral, spatial, and temporal resolutions of remote sensors have been continuously improving, making geospatial monitoring more accurate and comprehensive than ever before. To tackle this issue, various variable selection methods (e.g., filter, wrapper, and embedded methods) are nowadays used to reduce data complexity, and hence improve classification accuracy. Therefore, the goal of this research was twofold. Firstly, to assess the performance of the random forest (RF) classifier in a large heterogeneous landscape with diverse land-cover categories using multi-seasonal Sentinel imagery (i.e., Sentinel-1; S1 and Sentinel-2; S2) and ancillary data. Secondly, to compare RF variable selection methods to identify a subset of predictor variables that will be included in a final, simpler model. Using mean decrease accuracy (MDA) as a feature selection (FS) method, an original dataset was reduced from 114 to 34 input features, and its classification performance outperformed all-feature (114 features) and band-only (36 features) model with an OA of 90.91%. The most pertinent input features for vegetation mapping were S2 spectral bands (14 features), followed by the spectral indices derived from S2, texture features, and S1 bands. This research improved vegetation mapping by integrating radar and optical imagery, especially after applying FS methods which removed redundant and noisy features from the original dataset. Future research should address additional feature selection methods (i.e., filter, wrapper, or the embedded) for vegetation mapping, combined with advanced deep learning methods.
43
Sedlak, René, Andreas Welscher, Patrick Hannawald, Sabine Wüst, Rainer Lienhart e Michael Bittner. "Analysis of 2D airglow imager data with respect to dynamics using machine learning". Atmospheric Measurement Techniques 16, n. 12 (26 giugno 2023): 3141–53. http://dx.doi.org/10.5194/amt-16-3141-2023.
Abstract (sommario):
Abstract. We demonstrate how machine learning can be easily applied to support the analysis of large quantities of excited hydroxyl (OH*) airglow imager data. We use a TCN (temporal convolutional network) classification algorithm to automatically pre-sort images into the three categories “dynamic” (images where small-scale motions like turbulence are likely to be found), “calm” (clear-sky images with weak airglow variations) and “cloudy” (cloudy images where no airglow analyses can be performed). The proposed approach is demonstrated using image data of FAIM 3 (Fast Airglow IMager), acquired at Oberpfaffenhofen, Germany, between 11 June 2019 and 25 February 2020, achieving a mean average precision of 0.82 in image classification. The attached video sequence demonstrates the classification abilities of the learned TCN. Within the dynamic category, we find a subset of 13 episodes of image series showing turbulence. As FAIM 3 exhibits a high spatial (23 m per pixel) and temporal (2.8 s per image) resolution, turbulence parameters can be derived to estimate the energy diffusion rate. Similarly to the results the authors found for another FAIM station (Sedlak et al., 2021), the values of the energy dissipation rate range from 0.03 to 3.18 W kg−1.
44
Feng, Ruyi, Lizhe Wang e Yanfei Zhong. "Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery". Remote Sensing 10, n. 10 (25 settembre 2018): 1546. http://dx.doi.org/10.3390/rs10101546.
Abstract (sommario):
Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the optimal subset of signatures in this spectral library using the sparse regression technique, and has greatly improved the estimation of fractional abundances in ubiquitous mixed pixels. Since the potentially large standard spectral library can be given a priori, the most challenging task is to compute the regression coefficients, i.e., the fractional abundances, for the linear regression problem. There are many mathematical techniques that can be used to deal with the spectral unmixing problem; e.g., ordinary least squares (OLS), constrained least squares (CLS), orthogonal matching pursuit (OMP), and basis pursuit (BP). However, due to poor prediction accuracy and non-interpretability, the traditional methods often cannot obtain satisfactory estimations or achieve a reasonable interpretation. In this paper, to improve the regression accuracy of sparse unmixing, least angle regression-based constrained sparse unmixing (LARCSU) is introduced to further enhance the precision of sparse unmixing. Differing from the classical greedy algorithms and some of the cautious sparse regression-based approaches, the LARCSU algorithm has two main advantages. Firstly, it introduces an equiangular vector to seek the optimal regression steps based on the simple underlying geometry. Secondly, unlike the alternating direction method of multipliers (ADMM)-based algorithms that introduce one or more multipliers or augmented terms during their optimization procedures, no parameters are required in the computational process of the LARCSU approach. The experimental results obtained with both simulated datasets and real hyperspectral images confirm the effectiveness of LARCSU compared with the current state-of-the-art spectral unmixing algorithms. LARCSU can obtain a better fractional abundance map, as well as a higher unmixing accuracy, with the same order of magnitude of computational effort as the CLS-based methods.
45
Winiwarter, Lukas, Nicholas C. Coops, Alex Bastyr, Jean-Romain Roussel, Daisy Q. R. Zhao, Clayton T. Lamb e Adam T. Ford. "Extraction of Forest Road Information from CubeSat Imagery Using Convolutional Neural Networks". Remote Sensing 16, n. 6 (20 marzo 2024): 1083. http://dx.doi.org/10.3390/rs16061083.
Abstract (sommario):
Forest roads provide access to remote wooded areas, serving as key transportation routes and contributing to human impact on the local environment. However, large animals, such as bears (Ursus sp.), moose (Alces alces), and caribou (Rangifer tarandus caribou), are affected by their presence. Many publicly available road layers are outdated or inaccurate, making the assessment of landscape objectives difficult. To address these gaps in road location data, we employ CubeSat Imagery from the Planet constellation to predict the occurrence of road probabilities using a SegNet Convolutional Neural Network. Our research examines the potential of a pre-trained neural network (VGG-16 trained on ImageNet) transferred to the remote sensing domain. The classification is refined through post-processing, which considers spatial misalignment and road width variability. On a withheld test subset, we achieve an overall accuracy of 99.1%, a precision of 76.1%, and a recall of 91.2% (F1-Score: 83.0%) after considering these effects. We investigate the performance with respect to canopy coverage using a spectral greenness index, topography (slope and aspect), and land cover metrics. Results found that predictions are best in flat areas, with low to medium canopy coverage, and in the forest (coniferous and deciduous) land cover classes. The results are vectorized into a drivable road network, allowing for vector-based routing and coverage analyses. Our approach digitized 14,359 km of roads in a 23,500 km2 area in British Columbia, Canada. Compared to a governmental dataset, our method missed 10,869 km but detected an additional 5774 km of roads connected to the network. Finally, we use the detected road locations to investigate road age by accessing an archive of Landsat data, allowing spatiotemporal modelling of road access to remote areas. This provides important information on the development of the road network over time and the calculation of impacts, such as cumulative effects on wildlife.
46
Lv, Chengzhe, Yuefeng Lu, Miao Lu, Xinyi Feng, Huadan Fan, Changqing Xu e Lei Xu. "A Classification Feature Optimization Method for Remote Sensing Imagery Based on Fisher Score and mRMR". Applied Sciences 12, n. 17 (2 settembre 2022): 8845. http://dx.doi.org/10.3390/app12178845.
Abstract (sommario):
In object-oriented remote sensing image classification experiments, the dimension of the feature space is often high, leading to the “dimension disaster”. If a reasonable feature selection method is adopted, the classification efficiency and accuracy of the classifier can be improved. In this study, we took GF-2 remote sensing imagery as the research object and proposed a feature dimension reduction algorithm combining the Fisher Score and the minimum redundancy maximum relevance (mRMR) feature selection method. First, the Fisher Score was used to construct a feature index importance ranking, following which the mRMR algorithm was used to select the features with the maximum correlation and minimum redundancy between categories. The feature set was optimized using this method, and remote sensing images were automatically classified based on the optimized feature subset. Experimental analysis demonstrates that, compared with the traditional mRMR, Fisher Score, and ReliefF methods, the proposed Fisher Score–mRMR (Fm) method provides higher accuracy in remote sensing image classification. In terms of classification accuracy, the accuracy of the Fm feature selection method with RT and KNN classifiers is improved compared with that of single feature selection method, reaching 95.18% and 96.14%, respectively, and the kappa coefficient reaches 0.939 and 0.951, respectively.
47
Imangholiloo, Mohammad, Ville Luoma, Markus Holopainen, Mikko Vastaranta, Antti Mäkeläinen, Niko Koivumäki, Eija Honkavaara e Ehsan Khoramshahi. "A New Approach for Feeding Multispectral Imagery into Convolutional Neural Networks Improved Classification of Seedlings". Remote Sensing 15, n. 21 (3 novembre 2023): 5233. http://dx.doi.org/10.3390/rs15215233.
Abstract (sommario):
Tree species information is important for forest management, especially in seedling stands. To mitigate the spectral admixture of understory reflectance with small and lesser foliaged seedling canopies, we proposed an image pre-processing step based on the canopy threshold (Cth) applied on drone-based multispectral images prior to feeding classifiers. This study focused on (1) improving the classification of seedlings by applying the introduced technique; (2) comparing the classification accuracies of the convolutional neural network (CNN) and random forest (RF) methods; and (3) improving classification accuracy by fusing vegetation indices to multispectral data. A classification of 5417 field-located seedlings from 75 sample plots showed that applying the Cth technique improved the overall accuracy (OA) of species classification from 75.7% to 78.5% on the Cth-affected subset of the test dataset in CNN method (1). The OA was more accurate in CNN (79.9%) compared to RF (68.3%) (2). Moreover, fusing vegetation indices with multispectral data improved the OA from 75.1% to 79.3% in CNN (3). Further analysis revealed that shorter seedlings and tensors with a higher proportion of Cth-affected pixels have negative impacts on the OA in seedling forests. Based on the obtained results, the proposed method could be used to improve species classification of single-tree detected seedlings in operational forest inventory.
48
Sekandari, Milad, e Amin Beiranvand Pour. "Fuzzy Logic Modeling for Integrating the Thematic Layers Derived from Remote Sensing Imagery: A Mineral Exploration Technique". Environmental Sciences Proceedings 6, n. 1 (25 febbraio 2021): 8. http://dx.doi.org/10.3390/iecms2021-09349.
Abstract (sommario):
In this study, fuzzy logic modeling was implemented to fuse the thematic layers derived from principal components analysis (PCA) in order to generate mineral prospectivity maps. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing data were used. A spatial subset zone of the Central Iranian Terrane (CIT), Iran was selected in this study. The PCA technique was implemented for the processing of the datasets and for the production of alteration thematic layers. PCA4, PCA5, and PCA8 were selected as the most rational alteration thematic layers of ASTER for the generation of a prospectivity map. The fuzzy gamma operator was used to fuse the selected alteration thematic layers. The PCA3, PCA4, and PCA6 thematic layers (most rational alteration thematic layers) of WV-3 were fused using the fuzzy AND operator. Field reconnaissance, X-ray diffraction (XRD) analysis, and Analytical Spectral Devices (ASD) spectroscopy were carried out to verify the image processing results. Subsequently, mineral prospectivity maps were produced showing high-potential zones of Pb-Zn mineralization in the study area.
49
Khare, Siddhartha, Hooman Latifi, Sergio Rossi e Sanjay Kumar Ghosh. "Fractional Cover Mapping of Invasive Plant Species by Combining Very High-Resolution Stereo and Multi-Sensor Multispectral Imageries". Forests 10, n. 7 (27 giugno 2019): 540. http://dx.doi.org/10.3390/f10070540.
Abstract (sommario):
Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R2 values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions.
50
Chen, Nian, Kezhong Lu e Hao Zhou. "A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy". Computational Intelligence and Neuroscience 2021 (22 giugno 2021): 1–14. http://dx.doi.org/10.1155/2021/5592323.
Abstract (sommario):
A band selection method based on two layers selection (TLS) strategy, which forms an optimal subset from all-bands set to reconstitute the original hyperspectral imagery (HSI) and aims to cost a fewer bands for better performances, is proposed in this paper. As its name implies, TLS picks out the bands with low correlation and a large amount of information into the target set to reach dimensionality reduction for HSI via two phases. Specifically, the fast density peaks clustering (FDPC) algorithm is used to select the most representative node in each cluster to build a candidate set at first. During the implementation, we normalize the local density and relative distance and utilize the dynamic cutoff distance to weaken the influence of density so that the selection is more likely to be carried out in scattered clusters than in high-density ones. After that, we conduct a further selection in the candidate set using mRMR strategy and comprehensive measurement of information (CMI), and the eventual winners will be selected into the target set. Compared with other six state-of-the-art unsupervised algorithms on three real-world HSI data sets, the results show that TLS can group the bands with lower correlation and richer information and has obvious advantages in indicators of overall accuracy (OA), average accuracy (AA), and Kappa coefficient.