Largescale remote sensing image processing and analysis. Remote sensing of fractional green vegetation cover using. Remote sensing of environment university of maryland. Fusion of multisensor remote sensing data for urban land. Apr 16, 2019 remote sensing of landscapes with spectral images is designed as a textbook and reference for graduate students and professionals in a variety of disciplines including ecology, forestry, geology, geography, urban planning, archeology, and civil engineering, who want to use spectral images to help solve problems in the field. This sensor type has been widely used in a variety of remote sensing applications from environmental monitoring to military surveillance for several decades 1, 2. Airborne imaging spectroscopy the aviris imaging spectrometer green et al.
China b department of geography, university at buffalo, the state university of new york, 105 wilkeson quad. Spectral mixture analysis has been an important research topic in remote sensing applications, particularly for hyperspectral remote sensing data processing. Remote sensing can be defined as any process whereby information is. Unmixing via sparse representation can be reformulated as. Automatic extraction of optimal endmembers from airborne. The present analysis focused on two of those dates. Hyperspectral endmember extraction using spatially weighted. It aims to decompose a mixed pixel into a collection of spectrally pure components called endmembers, and their corresponding proportions called fractional abundances. Endmembers also play an important role in various fields, including classification 3 5, target or anomaly detection 6 8 and environmental monitoring and risk. These remote sensingbased approaches have proven effective for. A challenging task underlying many hyperspectral im.
Very often, the resolution cell corresponding to a single pixel in an image contains several substances endmembers 3. In recent years, many studies have revealed that unmixing using spectral. Hyperspectral remote sensing technology can obtain abundant spectral information to identify and distinguish spectrally unique materials, and it is able to provide a large amount of images for various thematic applications bioucasdias et al. Crop yield estimation based on unsupervised linear unmixing. Ieee transactions on geoscience and remote sensing 42, 20852095 2004. As such, for the abovementioned applications in the field of hyperspectral remote sensing, endmembers normally correspond to familiar. Remote sensing of landscapes with spectral images a. Mapping shellfish distribution using hyperspectral remote sensing. Crop yield estimation based on unsupervised linear. Use of remote sensing american university of beirut. Remote sensing image visualization using double layers abstract. Review incorporating spatial information in spectral unmixing. Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from modis. Asmentioned above, the major computational taskof mvsa is the solution of a quadratic problem with linear equality and.
In chapter 1, uav remote sensing technology with high spatial and temporal resolutions is considered critical for. Ieeetransactions on geoscience and remote sensing 1 hyperspectral data geometrybased estimation of number of endmembers using pnormbased pure pixel identi. Mapping shellfish distribution using hyperspectral remote sensing by jeffrey s. Remote sensing data the image investigated in this chapter was obtained by hyperion sensor boarded on eo1 satellite in november 11, 2004, and. History of remote sensing the knowledge about the history of remote sensing is necessary for better understanding of the subject and its scope, and also for future development, particularly for the welfare of human society.
Furthermore, while it is common to think of endmembers as invariant, i. The spectral properties of these two endmembers are typically determined based on field measurements, estimated using additional data sources e. Endmember extraction of hyperspectral remote sensing images based on the ant colony optimization aco algorithm abstract. Remote sensing images are characterised by their spectral, spatial, radiometric, and temporal resolutions. Remote sensing of environment serves the remote sensing community with the publication of results on the theory, science, applications, and technology of remote sensing of earth resources and environment. Index termsant colony optimization aco, endmember extraction, hyperspectral remote sensing, mixed pixel.
An official journal of the remote sensing and photogrammetry society. Mapping shellfish distribution using hyperspectral remote. Department of ecosystem science and sustainability, colorado state university, nesb 108, 1499 campus delivery, fort collins, co 80523. Thus, remote sensingand thevariety of methods to process image data represent essential tools for the enhancement of traditional agricultural management strategies. The development of remote sensing over time can be broadly divided into following six phases. The amount of remote sensing images has increased dramatically, due to the recent advances in satellite technology. Is the technology of measuring the characteristics of an object or surface from a distance. Unfortunately, interpretation of remote sensing data from arid regions is particularly difficult. Three different methods of collecting endmembers for spectral unmixing are. Sparse autoencoder network for endmember extraction. Spatialspectral endmember extraction by multidimensional. Endmember extraction is a key step in spectral unmixing.
Hyperspectral unmixing is an important technique for remote sensing image exploitation. Oct 23, 2019 in spite of these challenges, uav remote sensing systems have proved useful for many different applications see chapters 1, 2, and 3. Vincent a majority of all baseline shellfish maps are currently produced through ground surveys and manual aerial photo interpretation, a tedious process which is timeconsuming and prone to human errors. Applications of hyperspectral remote sensing in urban regions published in. Comparison of remote sensing data to libraries of material spectra. By the first world war, cameras mounted on airplanes provided aerial views of fairly large surface areas that proved invaluable. Remote sensing image visualization using double layers. Image pixel spectra can be modeled as mixtures of reflectance spectra from such libraries smith et al. Spie 5239, remote sensing for environmental monitoring, gis applications, and geology iii, february 2004.
Ieee journal of selected topics in applied earth observations and remote sensing 1 estimation of the number of endmembers using robust outlier detection method charoula andreou, student member, ieee, and vassilia karathanassi abstractthis paper introduces a novel approach for estimating the numbers of endmembers in hyperspectral imagery. One of the major characteristics of a remotely sensed image is the wavelength region it represents in the ems. Noisy images, lowresolution images, mixed pixel images most of the remote sensing images are untagged. Remote sensing as a technology can be said to have started with the appearance of the first photographs. Process of remote sensing pdf because of the extreme importance of remote sensing as a data input to gis, it has.
Hyperspectral unmixing is one of the most prominent research topics for hyperspectral remote. In thissituation,thescatteredenergy is amixing oftheendmember spectra. Remote sensing image an overview sciencedirect topics. Finally, 17 gives a more general perspective on the exploitation of highperformance computing platforms in remote sensing studies, with a more extensive discussion on speci. Pdf an investigation on indicative endmember detection. The technology of modern remote sensing has a very long history, dating back to the end of the 19th century with the invention of the camera. Efficient algorithms for largescale remote sensing images. Spectral mixture analysis of aster images for examining the relationship between urban thermal features and biophysical descriptors in indianapolis, indiana, usa remote sensing of environment 104. Remote sensing is the process of acquiring datainformation about. Empirical automatic estimation of the number of endmembers in hyperspectral images bin luo.
It is possible to allow the types and numbers of pure spectral endmembers to vary from pixeltopixel for unmixing roberts et al. An unsupervised classification method for hyperspectral. In spite of these challenges, uav remote sensing systems have proved useful for many different applications see chapters 1, 2, and 3. Endmembers are spectrally pure signatures of a given land cover type or radiance emitted at a speci. Endmember extraction of hyperspectral remote sensing images. An investigation on indicative endmember detection methods in hyperspectral remote sensing hamid zekri1, majid m. Nonnegativematrixfactorizationbased hyperspectral unmixing with partially known endmembers lei tong, jun zhou,senior member,ieee, yuntao qian,member, ieee, xiao bai, and yongshenggao,senior member, ieee abstracthyperspectral unmixing is an important technique for estimating fractions of various materials from remote sensing imagery. Initially cameras were used to take photographs on the ground, which provided and still does a fascinating and exciting way to capture moments in time and keep a record of something that happened, which looked more realistic than a drawing or painting. Spectral resolution refers to the bandwidth and the sampling rate over which the sensor gathers information about the scene. Pure surface materials denoted by endmembers need to be known for spectral mixture analysis, which is a popular technique for analysing hyperspectral remote sensing data. The visualization method discussed in this article and referred to as the manual endmember selection method mesm is. An investigation on indicative endmember detection methods in. An investigation on indicative endmember detection methods.
Pdf mixed pixels are common in hyperspectral remote sensing images. Use of remote sensing in natural resource management. Spie 5239, remote sensing for environmental monitoring, gis applications, and geology iii, february. An unsupervised classification method for hyperspectral remote sensing image based on spectral data mining 145 3. Pdf on sep 15, 20, hamid zekri and others published an investigation on indicative endmember detection methods in hyperspectral remote sensing find, read and cite all the research you need. Remote sensing and geographical information system gis. Application g the final element of the remote sensing process is. Since different materialsobjects may be present in the area covered by a pixel, the pixel reflectance can be considered as the linear mixture of reflectance of these materialsobjects i. In chapter 1, uav remote sensing technology with high spatial and temporal resolutions is considered critical for highthroughput phenotyping and precision agriculture. N remote sensing, hyperspectral data is an essential imaging sensory output by which we gain insight into the earth system by utilizing information beyond the human visible spectrum. Remote sensing of landscapes with spectral images is designed as a textbook and reference for graduate students and professionals in a variety of disciplines including ecology, forestry, geology, geography, urban planning, archeology, and civil engineering, who want to use spectral images to help solve problems in the field. Remote sensing imagery has relatively low spatial resolution. Applications of hyperspectral remote sensing in urban regions. Remote sensing data the image investigated in this chapter was obtained by hyperion sensor boarded on eo1 satellite in november 11, 2004, and it covers the 0.
Endmember extraction of hyperspectral remote sensing. Abstract this paper examines the potential application of hyperspectral remote sensing for the analysis of complex urban scenes. Comparing remote sensing images in order to detect what has changed between two acquisitions locating a given target inside an image or following it in a video stream performing autocorrelation of an image in order to detect, for example, pseudoperiodic structures. Acrs 2000 sunil bhaskaran and bisun datt school of geography faculty of science and technology university of new south wales, 2052, australia tel. There are a number of spectral reflectance libraries available on the internet for different materials and applications in remote sensing. The lmm model has been widely used in the remote sensing community and can be expressed as see, e. Pdf spatialspectral endmember extraction for spaceborne. Nevertheless, most existing metrics do not possess strong physical. A method for manual endmember selection and spectral unmixing. Multiple endmember spectral mixture analysis mesma is a technique for estimating the proportion of each pixel that is covered by a series of known cover types in other words, it seeks to determine the likely composition of each image pixel. Thoroughly interdisciplinary, rse publishes on terrestrial, oceanic and atmospheric sensing. Urban areas go through phenological changes just the same way as a vegetation cycle, that have a spatio.253 512 822 484 483 133 1295 768 463 1094 1357 519 443 671 1169 99 1562 1068 1174 143 813 881 18 84 1124 468 563 370 609 973 1354 272