Relevant bibliographies by topics / Multiscale remote sensing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Multiscale remote sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 January 2023

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Journal articles on the topic "Multiscale remote sensing"

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Mesev, V. "MULTISCALE AND MULTITEMPORAL URBAN REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B2 (July 25, 2012): 17–21. http://dx.doi.org/10.5194/isprsarchives-xxxix-b2-17-2012.

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dos Santos, Jefersson Alex, Philippe-Henri Gosselin, Sylvie Philipp-Foliguet, Ricardo da S. Torres, and Alexandre Xavier Falao. "Multiscale Classification of Remote Sensing Images." IEEE Transactions on Geoscience and Remote Sensing 50, no. 10 (October 2012): 3764–75. http://dx.doi.org/10.1109/tgrs.2012.2186582.

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Li, Lingling, Pujiang Liang, Jingjing Ma, Licheng Jiao, Xiaohui Guo, Fang Liu, and Chen Sun. "A Multiscale Self-Adaptive Attention Network for Remote Sensing Scene Classification." Remote Sensing 12, no. 14 (July 10, 2020): 2209. http://dx.doi.org/10.3390/rs12142209.

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High-resolution optical remote sensing image classification is an important research direction in the field of computer vision. It is difficult to extract the rich semantic information from remote sensing images with many objects. In this paper, a multiscale self-adaptive attention network (MSAA-Net) is proposed for the optical remote sensing image classification, which includes multiscale feature extraction, adaptive information fusion, and classification. In the first part, two parallel convolution blocks with different receptive fields are adopted to capture multiscale features. Then, the squeeze process is used to obtain global information and the excitation process is used to learn the weights in different channels, which can adaptively select useful information from multiscale features. Furthermore, the high-level features are classified by many residual blocks with an attention mechanism and a fully connected layer. Experiments were conducted using the UC Merced, NWPU, and the Google SIRI-WHU datasets. Compared to the state-of-the-art methods, the MSAA-Net has great effect and robustness, with average accuracies of 94.52%, 95.01%, and 95.21% on the three widely used remote sensing datasets.
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Wulamu, Aziguli, Zuxian Shi, Dezheng Zhang, and Zheyu He. "Multiscale Road Extraction in Remote Sensing Images." Computational Intelligence and Neuroscience 2019 (July 10, 2019): 1–9. http://dx.doi.org/10.1155/2019/2373798.

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Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field. On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images. Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points. Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.
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Vannier, Clémence, Chloé Vasseur, Laurence Hubert-Moy, and Jacques Baudry. "Multiscale ecological assessment of remote sensing images." Landscape Ecology 26, no. 8 (July 6, 2011): 1053–69. http://dx.doi.org/10.1007/s10980-011-9626-y.

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Wang, Yong, Wenkai Zhang, Zhengyuan Zhang, Xin Gao, and Xian Sun. "Multiscale Multiinteraction Network for Remote Sensing Image Captioning." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15 (2022): 2154–65. http://dx.doi.org/10.1109/jstars.2022.3153636.

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Wang, Yani, Jinfang Dong, and Bo Wang. "Feature Matching Optimization of Multimedia Remote Sensing Images Based on Multiscale Edge Extraction." Computational Intelligence and Neuroscience 2022 (June 2, 2022): 1–7. http://dx.doi.org/10.1155/2022/1764507.

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In order to solve the problem of low efficiency of image feature matching in traditional remote sensing image database, this paper proposes the feature matching optimization of multimedia remote sensing images based on multiscale edge extraction, expounds the basic theory of multiscale edge, and then registers multimedia remote sensing images based on the selection of optimal control points. In this paper, 100 remote sensing images with a size of 3619 ∗ 825 with a resolution of 30 m are selected as experimental data. The computer is configured with 2.9 ghz CPU, 16 g memory, and i7 processor. The research mainly includes two parts: image matching efficiency analysis of multiscale model; matching accuracy analysis of multiscale model and formulation of model parameters. The results show that when the amount of image data is large, feature matching takes more time. With the increase of sampling rate, the amount of image data decreases rapidly, and the feature matching time also shortens rapidly, which provides a theoretical basis for the multiscale model to improve the matching efficiency. The data size is the same, 3619 × 1825, which makes the matching time between images have little difference. Therefore, the matching time increases linearly with the increase of the number of images in the database. When the amount of image data in the database is large, a higher number of layers should be used; when the amount of image data in the database is small, the number of layers of the model should be reduced to ensure the accuracy of matching. The availability of the proposed method is proved.
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Cui, Hao, Peng Jia, Guo Zhang, Yong-Hua Jiang, Li-Tao Li, Jing-Yin Wang, and Xiao-Yun Hao. "Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images." IEEE Transactions on Geoscience and Remote Sensing 58, no. 4 (April 2020): 2308–23. http://dx.doi.org/10.1109/tgrs.2019.2947599.

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dos Santos, Philippe-Henri Gosselin, Sylvie Philipp-Foliguet, Ricardo da S. Torres, and Alexandre Xavier Falcao. "Interactive Multiscale Classification of High-Resolution Remote Sensing Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6, no. 4 (August 2013): 2020–34. http://dx.doi.org/10.1109/jstars.2012.2237013.

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Sheng Zheng, Wen-zhong Shi, Jian Liu, and Jinwen Tian. "Remote Sensing Image Fusion Using Multiscale Mapped LS-SVM." IEEE Transactions on Geoscience and Remote Sensing 46, no. 5 (May 2008): 1313–22. http://dx.doi.org/10.1109/tgrs.2007.912737.

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Dissertations / Theses on the topic "Multiscale remote sensing"

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Piles, Guillem Maria. "Multiscale soil moisture retrievals from microwave remote sensing observations." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/77910.

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La humedad del suelo es la variable que regula los intercambios de agua, energía, y carbono entre la tierra y la atmósfera. Mediciones precisas de humedad son necesarias para una gestión sostenible de los recursos hídricos, para mejorar las predicciones meteorológicas y climáticas, y para la detección y monitorización de sequías e inundaciones. Esta tesis se centra en la medición de la humedad superficial de la Tierra desde el espacio, a escalas global y regional. Estudios teóricos y experimentales han demostrado que la teledetección pasiva de microondas en banda L es optima para la medición de humedad del suelo, debido a que la atmósfera es transparente a estas frecuencias, y a la relación directa de la emisividad del suelo con su contenido de agua. Sin embargo, el uso de la teledetección pasiva en banda L ha sido cuestionado en las últimas décadas, pues para conseguir la resolución temporal y espacial requeridas, un radiómetro convencional necesitaría una gran antena rotatoria, difícil de implementar en un satélite. Actualmente, hay tres principales propuestas para abordar este problema: (i) el uso de un radiómetro de apertura sintética, que es la solución implementada en la misión Soil Moisture and Ocean Salinity (SMOS) de la ESA, en órbita desde noviembre del 2009; (ii) el uso de un radiómetro ligero de grandes dimensiones y un rádar operando en banda L, que es la solución que ha adoptado la misión Soil Moisture Active Passive (SMAP) de la NASA, con lanzamiento previsto en 2014; (iii) el desarrollo de técnicas de desagregación de píxel que permitan mejorar la resolución espacial de las observaciones. La primera parte de la tesis se centra en el estudio del algoritmo de recuperación de humedad del suelo a partir de datos SMOS, que es esencial para obtener estimaciones de humedad con alta precisión. Se analizan diferentes configuraciones con datos simulados, considerando (i) la opción de añadir información a priori de los parámetros que dominan la emisión del suelo en banda L —humedad, rugosidad, temperatura del suelo, albedo y opacidad de la vegetación— con diferentes incertidumbres asociadas, y (ii) el uso de la polarización vertical y horizontal por separado, o del primer parámetro de Stokes. Se propone una configuración de recuperación de humedad óptima para SMOS. La resolución espacial de los radiómetros de SMOS y SMAP (40-50 km) es adecuada para aplicaciones globales, pero limita la aplicación de los datos en estudios regionales, donde se requiere una resolución de 1-10 km. La segunda parte de esta tesis contiene tres novedosas propuestas de mejora de resolución espacial de estos datos: • Se ha desarrollado un algoritmo basado en la deconvolución de los datos SMOS que permite mejorar la resolución espacial de las medidas. Los resultados de su aplicación a datos simulados y a datos obtenidos con un radiómetro aerotransportado muestran que es posible mejorar el producto de resolución espacial y resolución radiométrica de los datos. • Se presenta un algoritmo para mejorar la resolución espacial de las estimaciones de humedad de SMOS utilizando datos MODIS en el visible/infrarrojo. Los resultados de su aplicación a algunas de las primeras imágenes de SMOS indican que la variabilidad espacial de la humedad del suelo se puede capturar a 32, 16 y 8 km. • Un algoritmo basado en detección de cambios para combinar los datos del radiómetro y el rádar de SMAP en un producto de humedad a 10 km ha sido desarrollado y validado utilizando datos simulados y datos experimentales aerotransportados. Este trabajo se ha desarrollado en el marco de las actividades preparatorias de SMOS y SMAP, los dos primeros satélites dedicados a la monitorización de la variación temporal y espacial de la humedad de la Tierra. Los resultados presentados contribuyen a la obtención de estimaciones de humedad del suelo con la precisión y la resolución espacial necesarias para un mejor conocimiento del ciclo del agua y una mejor gestión de los recursos hídricos.
Soil moisture is a key state variable of the Earth's system; it is the main variable that links the Earth's water, energy and carbon cycles. Accurate observations of the Earth's changing soil moisture are needed to achieve sustainable land and water management, and to enhance weather and climate forecasting skill, flood prediction and drought monitoring. This Thesis focuses on measuring the Earth's surface soil moisture from space at global and regional scales. Theoretical and experimental studies have proven that L-band passive remote sensing is optimal for soil moisture sensing due to its all-weather capabilities and the direct relationship between soil emissivity and soil water content under most vegetation covers. However, achieving a temporal and spatial resolution that could satisfy land applications has been a challenge to passive microwave remote sensing in the last decades, since real aperture radiometers would need a large rotating antenna, which is difficult to implement on a spacecraft. Currently, there are three main approaches to solving this problem: (i) the use of an L-band synthetic aperture radiometer, which is the solution implemented in the ESA Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009; (ii) the use of a large lightweight radiometer and a radar operating at L-band, which is the solution adopted by the NASA Soil Moisture Active Passive (SMAP) mission, scheduled for launch in 2014; (iii) the development of pixel disaggregation techniques that could enhance the spatial resolution of the radiometric observations. The first part of this work focuses on the analysis of the SMOS soil moisture inversion algorithm, which is crucial to retrieve accurate soil moisture estimations from SMOS measurements. Different retrieval configurations have been examined using simulated SMOS data, considering (i) the option of adding a priori information from parameters dominating the land emission at L-band —soil moisture, roughness, and temperature, vegetation albedo and opacity— with different associated uncertainties and (ii) the use of vertical and horizontal polarizations separately, or the first Stokes parameter. An optimal retrieval configuration for SMOS is suggested. The spatial resolution of SMOS and SMAP radiometers (~ 40-50 km) is adequate for global applications, but is a limiting factor to its application in regional studies, where a resolution of 1-10 km is needed. The second part of this Thesis contains three novel downscaling approaches for SMOS and SMAP: • A deconvolution scheme for the improvement of the spatial resolution of SMOS observations has been developed, and results of its application to simulated SMOS data and airborne field experimental data show that it is feasible to improve the product of the spatial resolution and the radiometric sensitivity of the observations by 49% over land pixels and by 30% over sea pixels. • A downscaling algorithm for improving the spatial resolution of SMOS-derived soil moisture estimates using higher resolution MODIS visible/infrared data is presented. Results of its application to some of the first SMOS images show the spatial variability of SMOS-derived soil moisture observations is effectively captured at the spatial resolutions of 32, 16, and 8 km. • A change detection approach for combining SMAP radar and radiometer observations into a 10 km soil moisture product has been developed and validated using SMAP-like observations and airborne field experimental data. This work has been developed within the preparatory activities of SMOS and SMAP, the two first-ever satellites dedicated to monitoring the temporal and spatial variation on the Earth's soil moisture. The results presented contribute to get the most out of these vital observations, that will further our understanding of the Earth's water cycle, and will lead to a better water resources management.
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Atherton, Jon Mark. "Multiscale remote sensing of plant physiology and carbon uptake." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6219.

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This study investigated the use of optical remote sensing for estimating leaf and canopy scale light use efficiency (LUE) and carbon exchange. In addition, a new leaf level model capable of predicting dynamic changes in apparent reflectance due to chlorophyll fluorescence was developed. A leaf level study was conducted to assess the applicability of passive remote sensing as a tool to measure the reduction, and the subsequent recovery, of photosynthetic efficiency during the weeks following transplantation. Spectral data were collected on newly planted saplings for a period of 8 weeks, as well as gas exchange measurements of LUE and PAM fluorescence measurements. A set of spectral indices, including the Photochemical Reflectance Index (PRI), were calculated from the reflectance measurements. A marked depression in photosynthetic rate occurred in the weeks after outplanting followed by a gradual increase, with recovery occurring in the later stages of the experimental period. As with photosynthetic rate, there was a marked trend in PRI values over the study period but no trend was observed in chlorophyll based indices. The study demonstrated that hyperspectral remote sensing has the potential to be a useful tool in the detection and monitoring of the dynamic effects of transplant shock. Relationships between hyperspectral reflectance indices, airborne carbon exchange measurements and satellite observations of ground cover were then explored across a heterogeneous Arctic landscape. Measurements were collected during August 2008, using the University of Edinburgh’s research aircraft, from an Arctic forest tundra zone in northern Finland as part of the Arctic Biosphere Atmosphere Coupling at Multiple Scales (ABACUS) study. Surface fluxes of CO2 were calculated using the eddy covariance method from airborne data that were collected from the same platform as hyperspectral reflectance measurements. Airborne CO2 fluxes were compared to MODIS vegetation indices. In addition, LUE was estimated from airborne flux data and compared to airborne measurements of PRI. There were no significant relationships between MODIS vegetation indices and airborne flux observations. There were weak to moderate (R2 = 0.4 in both cases) correlations between PRI and LUE and between PRI and incident radiation. A new coupled physiological radiative transfer model that predicts changes in the apparent reflectance of a leaf, due to chlorophyll fluorescence, was developed. The model relates a physically observable quantity, chlorophyll fluorescence, to the sub leaf level processes that cause the emission. An understanding of the dynamics of the processes that control fluorescence emission on multiple timescales should aid in the interpretation of this complex signal. A Markov Chain Monte Carlo (MCMC) algorithm was used to optimise biochemical model parameters by fitting model simulations of transient chlorophyll fluorescence to measured reflectance spectra. The model was then validated against an independent data set. The model was developed as a precursor to a full canopy scheme. To scale to the canopy and to use the model on trans-seasonal time scales, the effects of temperature and photoinhibition on the model biochemistry needs to be taken into account, and a full canopy radiative transfer scheme, such as FluorMOD, must be developed.
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Nguyen, Uyen. "Multiscale Remote Sensing Analysis To Monitor Riparian And Upland Semiarid Vegetation." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556735.

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The health of natural vegetation communities is of concern due to observed changes in the climatic-hydrological regime and land cover changes particularly in arid and semiarid regions. Monitoring vegetation at multi temporal and spatial scales can be the most informative approach for detecting change and inferring causal agents of change and remediation strategies. Riparian communities are tightly linked to annual stream hydrology, ground water elevations and sediment transport. These processes are subject to varying magnitudes of disturbance overtime and are candidates for multi-scale monitoring. My first research objective focused on the response of vegetation in the Upper San Pedro River, Arizona, to reduced base flows and climate change. I addressed the correlation between riparian vegetation and hydro-climate variables during the last three decades in one of the remaining undammed rivers in the southwestern U.S. Its riparian forest is threatened by the diminishing base flows, attributed by different studies either to increases in evapotranspiration (ET) due to conversion of grasslands to mesquite shrublands in the adjacent uplands, or to increased regional groundwater pumping to serve growing populations in surrounding urban areas and or to some interactions of those causes. Landsat 5 imagery was acquired for pre- monsoon period, when riparian trees had leafed out but before the arrival of summer monsoon rains in July. The result has showed Normalized Difference Vegetation Index (NDVI) values from both Landsat and Moderate Resolution Imaging Spectrometer (MODIS) had significant decreases which positively correlated to river flows, which decreased over the study period, and negatively correlated with air temperatures, which have increased by about 1.4°C from 1904 to the present. The predictions from other studies that decreased river flows could negatively impact the riparian forest were supported by this study. The pre-monsoon Normalized Different Vegetation Index (NDVI) average values in the adjacent uplands also decreased over thirty years and were correlated with the previous year's annual precipitation. Hence an increase in ET in the uplands did not appear to be responsible for the decrease in river flows in this study, leaving increased regional groundwater pumping as a feasible alternative explanation for decreased flows and deterioration of the riparian forest. The second research objective was to develop a new method of classification using very high-resolution aerial photo to map riparian vegetation at the species level in the Colorado River Ecosystem, Grand Canyon area, Arizona. Ground surveys have showed an obvious trend in which non-native saltcedar (Tamarix spp.) has replaced native vegetation over time. Our goal was to develop a quantitative mapping procedure to detect changes in vegetation as the ecosystem continues to respond to hydrological and climate changes. Vegetation mapping for the Colorado River Ecosystem needed an updated database map of the area covered by riparian vegetation and an indicator of species composition in the river corridor. The objective of this research was to generate a new riparian vegetation map at species level using a supervised image classification technique for the purpose of patch and landscape change detection. A new classification approach using multispectral images allowed us to successfully identify and map riparian species coverage the over whole Colorado River Ecosystem, Grand Canyon area. The new map was an improvement over the initial 2002 map since it reduced fragmentation from mixed riparian vegetation areas. The most dominant tree species in the study areas is saltcedar (Tamarix spp.). The overall accuracy is 93.48% and the kappa coefficient is 0.88. The reference initial inventory map was created using 2002 images to compare and detect changes through 2009. The third objective of my research focused on using multiplatform of remote sensing and ground calibration to estimate the effects of vegetation, land use patterns and water cycles. Climate change, hydrological and human uses are also leading to riparian, upland, grassland and crop vegetation changes at a variety of temporal and spatial scales, particularly in the arid and semi-arid ecosystems, which are more sensitive to changes in water availability than humid ecosystems. The objectives of these studies from the last three articles were to evaluate the effect of water balance on vegetation indices in different plant communities based on relevant spatial and temporal scales. The new methodology of estimating water requirements using remote sensing data and ground calibration with flux tower data has been successfully tested at a variety sites, a sparse desert shrub environment as well as mixed riparian and cropland systems and upland vegetation in the arid and semi-arid regions. The main finding form these studies is that vegetation-index methods have to be calibrated with ground data for each new ecosystem but once calibrated they can accurately scale ET over wide areas and long time spans.
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Wright, Graeme L. "Multiscale remote sensing for assessment of environmental change in the rural-urban fringe." Thesis, Curtin University, 2000. http://hdl.handle.net/20.500.11937/1110.

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The objective of this study was to investigate the application of multiscale satellite remote sensing data for assessment of land cover change in the rural-urban fringe. Inherent in this assessment process was the interpretation of multispectral data collected by several medium resolution satellite systems and evaluation of the quality of the resulting change information. Each dataset was acquired for a single date and classified at two levels of detail using standard classification algorithms. The optimum classification approach for each date was identified and the changes in land cover evaluated in several ways. The contribution of spatial and thematic errors and their propagation through the analysis process was investigated.Data for this research were acquired over an area approximately 4.5 km square located in the southern metropolitan area of Perth, Western Australia. At the time of the initial data acquisition in 1972 the area was predominantly rural and comprised mostly dense pine plantations, however by the final stages of data acquisition in 1991, the area was almost completely given over to urban residential land use. Changes were interpreted from classified Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM) and SPOT (System Pour l'Observation de la Terre) High Resolution Visible (HRV) multispectral data, and were compared to reference maps compiled from medium scale aerial photographs. The geometric properties of high resolution panchromatic IRS1-D data were also evaluated to test the geometric potential of high resolution satellite data.Supervised and unsupervised classification algorithms were used for derivation of land cover maps from each multispectral dataset at two levels of detail. Data were classified onto four general levels at the broadest (Level I) classification, and into nine levels at the finest (Level II) classification. The Kappa statistic and its variance were used to determine the optimum classification approach for each dataset and at each level of detail. No significant differences were observed between classification techniques at Level I, however at Level II the supervised classification approach produced significantly better results for the Landsat TM and SPOT HRV data. Classification at the more general Level I did not produce substantially higher classification rates compared to the same data at Level II. Additionally, higher spatial resolution data did not provide increased accuracy, however this was due mainly to a much greater complexity of land covers present at the time the higher resolution Landsat TM and SPOT HRV data were recorded.Land cover changes were assessed separately at Level I for all datasets, and also between Landsat TM and SPOT HRV data at Level II. Integrated multiscale assessment of land cover change was undertaken using classified Landsat MSS data at Level I and Landsat TM data at Level 11. This enabled the continuity of change to be established across classification levels and sensor systems, even though there were variations in the level of detail extracted from each image.The sources of spatial and thematic errors in the data were investigated and their effects on change assessment analysed. The evaluation of high resolution panchromatic satellite data emphasised the contribution to the analysis of spatial errors contained within the reference data. The multiscale data also indicated that combined propagation of spatial and thematic errors requires investigation using appropriate simulation modelling to establish the influence of data uncertainty on classification and change assessment results.This research provides useful results for demonstrating a process for the integration of information derived from remotely sensed data at different measurement scales. Availability of data from an increasing range of remote sensing platforms and uncertainty of long term data availability emphasises the need to develop flexible interpretation and analysis approaches. This research adds value to the existing data archive by demonstrating how historical data may be integrated regardless of the spectral and spatial characteristics of the sensors.
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Fieguth, Paul Werner 1968. "Application of multiscale estimation to large scale multidimensional imaging and remote sensing problems." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11409.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Vita.
Includes bibliographical references (p. 287-298).
by Paul Werner Fieguth.
Ph.D.
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Wright, Graeme L. "Multiscale remote sensing for assessment of environmental change in the rural-urban fringe." Curtin University of Technology, School of Spatial Sciences, 2000. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=10384.

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The objective of this study was to investigate the application of multiscale satellite remote sensing data for assessment of land cover change in the rural-urban fringe. Inherent in this assessment process was the interpretation of multispectral data collected by several medium resolution satellite systems and evaluation of the quality of the resulting change information. Each dataset was acquired for a single date and classified at two levels of detail using standard classification algorithms. The optimum classification approach for each date was identified and the changes in land cover evaluated in several ways. The contribution of spatial and thematic errors and their propagation through the analysis process was investigated.Data for this research were acquired over an area approximately 4.5 km square located in the southern metropolitan area of Perth, Western Australia. At the time of the initial data acquisition in 1972 the area was predominantly rural and comprised mostly dense pine plantations, however by the final stages of data acquisition in 1991, the area was almost completely given over to urban residential land use. Changes were interpreted from classified Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM) and SPOT (System Pour l'Observation de la Terre) High Resolution Visible (HRV) multispectral data, and were compared to reference maps compiled from medium scale aerial photographs. The geometric properties of high resolution panchromatic IRS1-D data were also evaluated to test the geometric potential of high resolution satellite data.Supervised and unsupervised classification algorithms were used for derivation of land cover maps from each multispectral dataset at two levels of detail. Data were classified onto four general levels at the broadest (Level I) classification, and into nine levels at the finest (Level II) classification. The ++
Kappa statistic and its variance were used to determine the optimum classification approach for each dataset and at each level of detail. No significant differences were observed between classification techniques at Level I, however at Level II the supervised classification approach produced significantly better results for the Landsat TM and SPOT HRV data. Classification at the more general Level I did not produce substantially higher classification rates compared to the same data at Level II. Additionally, higher spatial resolution data did not provide increased accuracy, however this was due mainly to a much greater complexity of land covers present at the time the higher resolution Landsat TM and SPOT HRV data were recorded.Land cover changes were assessed separately at Level I for all datasets, and also between Landsat TM and SPOT HRV data at Level II. Integrated multiscale assessment of land cover change was undertaken using classified Landsat MSS data at Level I and Landsat TM data at Level 11. This enabled the continuity of change to be established across classification levels and sensor systems, even though there were variations in the level of detail extracted from each image.The sources of spatial and thematic errors in the data were investigated and their effects on change assessment analysed. The evaluation of high resolution panchromatic satellite data emphasised the contribution to the analysis of spatial errors contained within the reference data. The multiscale data also indicated that combined propagation of spatial and thematic errors requires investigation using appropriate simulation modelling to establish the influence of data uncertainty on classification and change assessment results.This research provides useful results for demonstrating a process for the integration of information derived from remotely sensed data at different measurement ++
scales. Availability of data from an increasing range of remote sensing platforms and uncertainty of long term data availability emphasises the need to develop flexible interpretation and analysis approaches. This research adds value to the existing data archive by demonstrating how historical data may be integrated regardless of the spectral and spatial characteristics of the sensors.
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Blessing, Sithole Vhusomuzi. "A multiscale remote sensing assessment of subtropical indigenous forests along the wild coast, South Africa." Thesis, Nelson Mandela Metropolitan University, 2015. http://hdl.handle.net/10948/d1021169.

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The subtropical forests located along South Africa’s Wild Coast region, declared as one of the biodiversity hotspots, provide benefits to the local and national economy. However, there is evidence of increased pressure exerted on the forests by growing population and reduced income from activities not related to forest products. The ability of remote sensing to quantify subtropical forest changes over time, perform species discrimination (using field spectroscopy) and integrating field spectral and multispectral data were all assessed in this study. Investigations were conducted at pixel, leaf and sub-pixel levels. Both per-pixel and sub-pixel classification methods were used for improved forest characterisation. Using SPOT 6 imagery for 2013, the study determined the best classification algorithm for mapping sub-tropical forest and other land cover types to be the maximum likelihood classifier. Maximum likelihood outperformed minimum distance, spectral angle mapper and spectral information divergence algorithms, based on overall accuracy and Kappa coefficient values. Forest change analysis was made based on spectral measurements made at top of the atmosphere (TOC) level. When applied to the 2005 and 2009 SPOT 5 images, subtropical forest changes between 2005-2009 and 2009-2013 were quantified. A temporal analysis of forest cover trends in the periods 2005-2009 and 2009-2013 identified a decreasing trend of -3648.42 and -946.98 ha respectively, which translated to 7.81 percent and 2.20 percent decrease. Although there is evidence of a trend towards decreased rates of forest loss, more conservation efforts are required to protect the Wild Coast ecosystem. Using field spectral measurements data, the hierarchical method (comprising One-way ANOVA with Bonferroni correction, Classification and Regression Trees (CART) and Jeffries Matusita method) successfully selected optimal wavelengths for species discrimination at leaf level. Only 17 out of 2150 wavelengths were identified, thereby reducing the complexities related to data dimensionality. The optimal 17 wavelength bands were noted in the visible (438, 442, 512 and 695 nm), near infrared (724, 729, 750, 758, 856, 936, 1179, 1507 and 1673 nm) and mid-infrared (2220, 2465, 2469 and 2482 nm) portions of the electromagnetic spectrum. The Jeffries-Matusita (JM) distance method confirmed the separability of the selected wavelength bands. Using these 17 wavelengths, linear discriminant analysis (LDA) classified subtropical species at leaf level more accurately than partial least squares discriminant analysis (PLSDA) and random forest (RF). In addition, the study integrated field-collected canopy spectral and multispectral data to discriminate proportions of semi-deciduous and evergreen subtropical forests at sub-pixel level. By using the 2013 land cover (using MLC) to mask non-forested portions before sub-pixel classification (using MTMF), the proportional maps were a product of two classifiers. The proportional maps show higher proportions of evergreen forests along the coast while semi-deciduous subtropical forest species were mainly on inland parts of the Wild Coast. These maps had high accuracy, thereby proving the ability of an integration of field spectral and multispectral data in mapping semi-deciduous and evergreen forest species. Overall, the study has demonstrated the importance of the MLC and LDA and served to integrate field spectral and multispectral data in subtropical forest characterisation at both leaf and top-of-atmosphere levels. The success of both the MLC and LDA further highlighted how essential parametric classifiers are in remote sensing forestry applications. Main subtropical characteristics highlighted in this study were species discrimination at leaf level, quantifying forest change at pixel level and discriminating semi-deciduous and evergreen forests at sub-pixel level.
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Magee, Kevin S. "Segmentation, Object-Oriented Applications for Remote Sensing Land Cover and Land Use Classification." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298040118.

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McCarthy, Laura Elaine 1960. "Impact of military maneuvers on Mojave Desert surfaces: A multiscale analysis." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/282131.

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Concern for environmental management of our natural resources is most often focused on the human impacts upon these resources. Minor stresses on surface materials in sensitive desert landscapes can greatly increase the rate and character of erosion. The National Training Center, Ft. Irwin, located in the middle of the Mojave Desert, California, provides a study area of intense off-road vehicle (ORV) activity spanning a 50-year period. This study documents a case of concentrated ORV activity on sensitive desert environments, and the resulting environmental impacts. Geomorphic surfaces from two study sites within the Ft. Irwin area were mapped from 1:28,400 scale black and white aerial photographs taken in 1947. Surface disruption attributed to military activity was then mapped for the same areas from 1993, 1:12,000, black and white aerial photographs. Several field checks were conducted to verify this mapping. Images created from SPOT panchromatic and Landsat Thematic Mapper (TM) multispectral data acquired during the spring of 1987 and 1993 were analyzed to assess both the extent of disrupted surfaces and the surface geomorphology discernable from satellite data. Classified and merged images were then created from these data and demonstrate the capabilities of satellite data to aid in the delineation of disrupted geomorphic surfaces. Correlations were also established between highly disrupted surfaces and soil surface conditions on selected geomorphic surfaces. Disruption maps produced from the air photos indicate that the amount of disrupted surfaces within the study sites grew from a combined total of 1.3 km² in 1947 to 33.4 km² by 1993. A combination of 6 bands of Landsat TM data with a seventh band of SPOT panchromatic data yielded a product that delineated broad geomorphic surfaces that closely correlate with those mapped from the aerial photography. An error matrix between these two products resulted in an overall accuracy of 83.36% and a Kappa Index of Agreement of 77.28%. A 15-class unsupervised classification of the SPOT panchromatic data produced the representation of the extent and levels of disruption present in the study areas that closely matched field observations. Field sampling of soil strength and clay/silt percentages on disturbed and undisturbed surfaces reveals that these arid land surfaces react to intense ORV activity by becoming more compact and exhibiting higher percentages of clays and silts.
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Umbert, Ceresuela Marta. "Exploiting the multiscale synergy among ocean variables : application to the improvement of remote sensing salinity maps." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/321115.

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Les imatges de teledetecció de la superfície oceànica proporcionen una vista sinòptica de la complexa geometria de la circulació oceànica, dominada per la variabilitat de mesoescala. Estructures com filaments i vòrtex són presents en els diferents escalars advectats pel flux oceànic. L’origen més probable d’aquestes estructures és el caràcter turbulent dels corrents, aquestes estructures són persistents amb el temps i compatibles amb la dinàmica mesoscalar oceànica. A escales espacials de quilòmetres o més, la turbulència és principalment 2D, i una complexa geometria, plena de filaments i remolins de mides diferents, emergeix en les imatges superficials de teledetecció de concentració de clorofil·la-a, salinitat superficial, així com en altres escalars més coneguts com són la temperatura superficial i la topografia dinàmica. L’objectiu d’aquesta tesi és explorar i aplicar metodologies de mapatge que permeten millorar la qualitat de mapes de teledetecció oceànica en general, i en particular de la salinitat superficial del mar (SSS). Les diferents metodologies emprades en aquesta tesi han estat aplicades amb l’objectiu específic de millorar els mapes de teledetecció de salinitat superficial del mar proveïts per la missió SMOS de l’Agència Espaial Europea. SMOS és el primer satèl·lit capaç de mesurar la humitat del sol i salinitat oceànica des de l’espai a escala global. La primera part d’aquesta tesi se centra a analitzar les característiques dels productes de nivell 2 (L2) de salinitat de SMOS i produir mapes de nivell 3 (L3) de salinitat utilitzant aproximacions clàssiques: millora del filtratge, mitjana ponderada i Interpolació Òptima. En el curs de la nostra recerca obtenim un conjunt de recomanacions de com processar les dades de SMOS començant des del nivell L2. Aquesta tesi també presenta una nova tècnica de fusió de dades que permet explotar les estructures turbulentes comunes entre diferents variables oceàniques, representant un pas endavant en la cadena de processat per generar mapes de nivell 4 (L4). Aquesta tècnica de fusió es basa teòricament en les propietats geomètriques dels traçadors advectats per la dinàmica oceànica (Turiel et al., 2005a). Degut a l’efecte de forta cissalla als fluits turbulents, l’estructura espacial d’un traçador oceànic hereta algunes propietats del flux subjacent, i en particular el seu arranjament geomètric. Com a conseqüència, les diferents variables oceàniques mostren propietats d’escala similars a la cascada d’energia turbulenta (Seuront and Schmitt, 2005; Nieves et al., 2007; Nieves and Turiel, 2009; Isern-Fontanet et al., 2007). El mètode de fusió agafa un senyal de menor qualitat (afectat per soroll, forats de dades i/o de resolució més baixa) i en millora la seva qualitat. A més d’això, el mètode de fusió és capaç d’extrapolar les dades de forma geofísicament coherent. Aquesta millora del senyal s’aconsegueix utilitzant una altra variable oceànica adquirida amb major qualitat, cobertura espacial més gran i/o millor resolució. Un punt clau d’aquesta aproximació és la suposició de l’existència d’una estructura multifractal de les imatges de teledetecció oceànica (Lovejoy et al., 2001b), i que les línies de singularitat de les diferents variables de l’oceà coincideixen. Sota aquestes premises, els gradients de les dues variables a fusionar estan relacionats per una matriu suau. Com a primera i simple aproximació, s’assumeix que aquesta matriu és proporcional a la identitat; això porta a un esquema de regressió lineal local. Aquesta tesi mostra que aquesta aproximació senzilla permet reduir l’error i millorar la cobertura del producte de nivell 4 resultant. D’altra banda, s’obté informació sobre la relació estadística entre les dues variables fusionades, ja que la dependència funcional entre elles es determina per cada punt de la imatge.
Remote sensing imagery of the ocean surface provides a synoptic view of the complex geometry of ocean circulation, which is dominated by mesoscale variability. The signature of filaments and vortices is present in different ocean scalars advected by the oceanic flow. The most probable origin of the observed structures is the turbulent character of ocean currents, and those signatures are persistent over time scales compatible with ocean mesoscale dynamics. At spatial scales of kilometers or more, turbulence is mainly 2D, and a complex geometry, full of filaments and eddies of different sizes, emerges in remote sensing images of surface chlorophyll-a concentration and surface salinity, as well as in other scalars acquired with higher quality such as surface temperature and absolute dynamic topography. The aim of this thesis is to explore and apply mapping methodologies to improve the quality of remote sensing maps in general, but focusing in the case of remotely sensed sea surface salinity (SSS) data. The different methodologies studied in this thesis have been applied with the specific goal of improving surface salinity maps generated from data acquired by the European Space Agency's mission SMOS, the first satellite able to measure soil moisture and ocean salinity from space at a global scale. The first part of this thesis will introduce the characteristics of the operational SMOS Level 2 (L2) SSS products and the classical approaches to produce the best possible SSS maps at Level 3 (L3), namely data filtering, weighted average and Optimal Interpolation. In the course of our research we will obtain a set of recommendations about how to process SMOS data starting from L2 data. A fusion technique designed to exploit the common turbulent signatures between different ocean variables is also explored in this thesis, in what represents a step forward from L3 to Level 4 (L4). This fusion technique is theoretically based on the geometrical properties of advected tracers. Due to the effect of the strong shear in turbulent flows, the spatial structure of tracers inherit some properties of the underlying flow and, in particular, its geometrical arrangement. As a consequence, different ocean variables exhibit scaling properties, similar to the turbulent energy cascade. The fusion method takes a signal affected by noise, data gaps and/or low resolution, and improves it in a geophysically meaningful way. This signal improvement is achieved by using an appropriate data, which is another ocean variable acquired with higher quality, greater spatial coverage and/or finer resolution. A key point in this approach is the assumption of the existence of a multifractal structure in ocean images, and that singularity lines of the different ocean variables coincide. Under these assumptions, the horizontal gradients of both variables, signal and template, can be related by a smooth matrix. The first, simplest approach to exploit such an hypothesis assumes that the relating matrix is proportional to the identity, leading to a local regression scheme. As shown in the thesis, this simple approach allows reducing the error and improving the coverage of the resulting Level 4 product; Moreover, information about the statistical relationship between the two fields is obtained since the functional dependence between signal and template is determined at each point.
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Books on the topic "Multiscale remote sensing"

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Multiscale hydrologic remote sensing: Perspectives and applications. Boca Raton: Taylor & Francis, 2012.

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National Research Council (U.S.). Water Science and Technology Board. and National Academies Press (U.S.), eds. Integrating multiscale observations of U.S. waters. Washington, D.C: National Academies Press, 2008.

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Multiscale Hydrologic Remote Sensing. Taylor & Francis Group, 2017.

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Hong, Yang, and Ni-Bin Chang. Multiscale Hydrologic Remote Sensing. Taylor & Francis Group, 2012.

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Hong, Yang, and Ni-Bin Chang. Multiscale Hydrologic Remote Sensing: Perspectives and Applications. Taylor & Francis Group, 2012.

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Hong, Yang, and Ni-Bin Chang. Multiscale Hydrologic Remote Sensing: Perspectives and Applications. Taylor & Francis Group, 2012.

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Hong, Yang, and Ni-Bin Chang. Multiscale Hydrologic Remote Sensing: Perspectives and Applications. Taylor & Francis Group, 2012.

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Hong, Yang, and Ni-Bin Chang. Multiscale Hydrologic Remote Sensing: Perspectives and Applications. Taylor & Francis Group, 2012.

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Book chapters on the topic "Multiscale remote sensing"

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Li, Zhe, Dawen Yang, Yang Hong, Bing Gao, and Qinghua Miao. "Multiscale Evaluation and Applications of Current Global Satellite Based Precipitation Products over the Yangtze River Basin." In Hydrologic Remote Sensing, 193–214. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315370392-12.

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Liang, Bingqing, and Qihao Weng. "Multiscale Fractal Characteristics of Urban Landscape in Indianapolis, USA." In Scale Issues in Remote Sensing, 230–52. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118801628.ch12.

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Silvan-Cárdenas, José L., and Le Wang. "Multiscale Approach for Ground Filtering from Lidar Altimetry Measurements." In Scale Issues in Remote Sensing, 265–84. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118801628.ch14.

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Hay, Geoffrey J. "Visualizing Scale-Domain Manifolds: A Multiscale Geo-Object-Based Approach." In Scale Issues in Remote Sensing, 139–69. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118801628.ch08.

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Hay, Geoffrey J., and Danielle J. Marceau. "Multiscale Object-Specific Analysis (MOSA): An Integrative Approach for Multiscale Landscape Analysis." In Remote Sensing Image Analysis: Including The Spatial Domain, 71–92. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2560-0_5.

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Bian, Ling. "Multiscale Nature of Spatial Data in Scaling Up Environmental Models." In Scale in Remote Sensing and GIS, 13–26. New York: Routledge, 2023. http://dx.doi.org/10.1201/9780203740170-2.

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Tzotsos, Angelos, Konstantinos Karantzalos, and Demetre Argialas. "Multiscale Segmentation and Classification of Remote Sensing Imagery with Advanced Edge and Scale-Space Features." In Scale Issues in Remote Sensing, 170–96. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118801628.ch09.

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Carvalho, Luis M. T. de, Fausto W. Acerbi, Jan G. P. W. Clevers, Leila M. G. Fonseca, and Steven M. de Jong. "Multiscale Feature Extraction from Images Using Wavelets." In Remote Sensing Image Analysis: Including The Spatial Domain, 237–70. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2560-0_13.

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Roberts, Dar, Michael Alonzo, Erin B. Wetherley, Kenneth L. Dudley, and Phillip E. Dennison. "9. Multiscale Analysis of Urban Areas Using Mixing Models." In Integrating Scale in Remote Sensing and GIS, 247–82. Routledge, 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge, 711 Third Avenue, New York, NY 10017: CRC Press, 2016. http://dx.doi.org/10.1201/9781315373720-10.

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Franklin, Janet, and Curtis E. Woodcock. "Multiscale Vegetation Data for the Mountains of Southern California: Spatial and Categorical Resolution." In Scale in Remote Sensing and GIS, 141–68. New York: Routledge, 2023. http://dx.doi.org/10.1201/9780203740170-8.

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Conference papers on the topic "Multiscale remote sensing"

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Caillault, Karine, Sandrine Fauqueux, Christophe Bourlier, and Pierre Simoneau. "Infrared multiscale sea surface modeling." In Remote Sensing, edited by Charles R. Bostater, Jr., Xavier Neyt, Stelios P. Mertikas, and Miguel Vélez-Reyes. SPIE, 2006. http://dx.doi.org/10.1117/12.689720.

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Nahum, Carole E. "Autofocusing using multiscale local correlation." In Remote Sensing, edited by Francesco Posa. SPIE, 1998. http://dx.doi.org/10.1117/12.331359.

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Martin, Vincent, and Arnaud Kelbert. "Multiscale statistical image destriping algorithm." In SPIE Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2015. http://dx.doi.org/10.1117/12.2195002.

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Galli, Luca, and Damiana de Candia. "Multispectral image segmentation via multiscale weighted aggregation method." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2005. http://dx.doi.org/10.1117/12.627534.

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Datcu, Mihai P., and Gintautas Palubinskas. "Multiscale Bayesian height estimation from InSAR using a fractal prior." In Remote Sensing, edited by Francesco Posa. SPIE, 1998. http://dx.doi.org/10.1117/12.331347.

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Moser, Gabriele, Elena Angiati, and Sebastiano B. Serpico. "Multiscale unsupervised change detection by Markov random fields and wavelet transforms." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2007. http://dx.doi.org/10.1117/12.737465.

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Tanelli, Simone, Luca Facheris, Fabrizio Cuccoli, and Dino Giuli. "Tracking radar echoes by multiscale correlation: a nowcasting weather radar application." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1999. http://dx.doi.org/10.1117/12.373261.

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Robin, A., S. Mascle-Le Hégarat, and L. Moisan. "A multiscale multitemporal land cover classification method using a Bayesian approach." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2005. http://dx.doi.org/10.1117/12.627604.

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Galli, Luca, Davide Passaro, and Serena Avolio. "A multiscale joint segmentation technique for multitemporal and multisource remote sensing images." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2007. http://dx.doi.org/10.1117/12.737741.

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Yang, Senlin, and Xin Chong. "Remote-sensing Fusion by Multiscale Block-based Compressed Sensing." In 2015 4th National Conference on Electrical, Electronics and Computer Engineering. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/nceece-15.2016.280.

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