MultiSpec—a tool for multispectral–hyperspectral image data analysis

Biehl, L. L.; Landgrebe, D. A.

doi:10.1016/s0098-3004(02)00033-x

Cited by 127 publications

(63 citation statements)

References 4 publications

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“…To determine the number of suitable variables for entering the artificial neural network, the correlation coefficients between the variables can be calculated [31], [32]. Variables such as altitude, forest cover and distance and proximity to residential areas were selected as network inputs.…”

Section: Combining the Potential Of Deforestation In A Database Of Spmentioning

confidence: 99%

Using GIS and Artificial Neural Network for Deforestation Prediction

Ahmadi¹

2018

Preprint

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Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.

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Section: Combining the Potential Of Deforestation In A Database Of Spmentioning

confidence: 99%

Using GIS and Artificial Neural Network for Deforestation Prediction

Ahmadi¹

2018

Preprint

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“…The Indian Pines dataset is available free of charge via Purdue University's website. 44 Figure 2 shows the scene and its reference ground truth data with 13 classes. Figure 3 shows the legend of the reference data, giving information about all the available classes in the scene in addition to the unclassified pixels (unknown class).…”

Section: Datasetmentioning

confidence: 99%

Spectral region identification versus individual channel selection in supervised dimensionality reduction of hyperspectral image data

Aria

Menenti

Gorte

2017

J. Appl. Remote Sens

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, "Spectral region identification versus individual channel selection in supervised dimensionality reduction of hyperspectral image data," J. Appl. Remote Sens. 11(4), 046010 (2017), doi: 10.1117/1.JRS.11.046010. Abstract. Hyperspectral images may be applied to classify objects in a scene. The redundancy in hyperspectral data implies that fewer spectral features might be sufficient for discriminating the objects captured in a scene. The availability of labeled classes of several areas in a scene paves the way for a supervised dimensionality reduction, i.e., using a discrimination measure between the classes in a scene to select spectral features. We show that averaging adjacent spectral channels and using wider spectral regions yield a better class separability than the selection of individual channels from the original hyperspectral dataset. We used a method named spectral region splitting (SRS), which creates a new feature space by averaging neighboring channels. In addition to the common benefits of channel selection methods, the algorithm constructs wider spectral regions when it is useful. Using different class separability measures over various datasets resulted in a better discrimination between the classes than the best-selected channels using the same measure. The reason is that the wider spectral regions led to a reduction in intraclass distances and an improvement in class discrimination. The overall classification accuracy of two hyperspectral scenes gave an increase of about twopercent when using the spectral regions determined by applying SRS. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…MultiSpec is a freeware software application for analyzing multispectral and imaging spectrometer (hyperspectral) data such as LANDSAT TM, SPOT, MODIS, ASTER, IKONOS, Quickbird, ALI, Hyperion or AVIRIS data to name a few [13,14]. MultiSpec is implemented for personal computers running the Apple Macintosh operating system (OS) including Intel, PowerPC or 680x0 based machines running OS7.1 thru OSX and the Microsoft Windows OS running Windows 95/98/NT/2000/ME/XP.…”

Section: E Multispecmentioning

confidence: 99%

Development and Implementation of Software Systems for Imaging Spectroscopy

Boardman

Biehl²,

Clark³

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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Abstract-Specialized software systems have played a crucial role throughout the twenty-five year course of the development of the new technology of imaging spectroscopy, or hyperspectral remote sensing. By their very nature, hyperspectral data place unique and demanding requirements on the computer software used to visualize, analyze, process and interpret them. Often described as a marriage of the two technologies of reflectance spectroscopy and airborne/spaceborne remote sensing, imaging spectroscopy, in fact, produces data sets with unique qualities, unlike previous remote sensing or spectrometer data. Because of these unique spatial and spectral properties hyperspectral data are not readily processed or exploited with legacy software systems inherited from either of the two parent fields of study. This paper provides brief reviews of seven important software systems developed specifically for imaging spectroscopy.

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MultiSpec—a tool for multispectral–hyperspectral image data analysis

Cited by 127 publications

References 4 publications

Using GIS and Artificial Neural Network for Deforestation Prediction

Using GIS and Artificial Neural Network for Deforestation Prediction

Spectral region identification versus individual channel selection in supervised dimensionality reduction of hyperspectral image data

Development and Implementation of Software Systems for Imaging Spectroscopy

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