&lt;title&gt;Integration of spatial and spectral information in unsupervised classification for multispectral and hyperspectral data&lt;/title&gt;

Jimenez-Rodriguez, Luis O.; Rivera-Medina, Jorge

doi:10.1117/12.373270

Cited by 6 publications

(2 citation statements)

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“…This type of processing can be approached from various points of view. In [30], a possibility is discussed for the refinement of results obtained by spectral techniques through a second step based on spatial context. The techniques proposed by these authors do not focus on endmember extraction, but yield some insightful feedback on plausible spatial and spectral response combinations.…”

Section: Background On Endmember Extraction Techniquesmentioning

confidence: 99%

Spatial/spectral endmember extraction by multidimensional morphological operations

Martı́nez

Pérez

Plaza

2002

IEEE Trans. Geosci. Remote Sensing

455

255

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Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.

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Section: Background On Endmember Extraction Techniquesmentioning

confidence: 99%

Spatial/spectral endmember extraction by multidimensional morphological operations

Martı́nez

Pérez

Plaza

2002

IEEE Trans. Geosci. Remote Sensing

455

255

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“…In recent years, non-parametric machine learning classifiers were also developed to improve classification accuracy, such as the classification and regression tree (CART) [14][15][16], support vector machine (SVM) [17][18][19], and random forest (RF) [20,21]. These classifiers are often used for hyperspectral data with more spectral information provided in each pixel [22,23]. However, the increase in within-class variance and decrease in between-class variance often limit the accuracy of pixel-based methods [24].…”

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Mapping Using RapidEye Imagery Based on a Novel Band Attention Deep Learning Method in the Three Gorges Reservoir Area

Xin

Tan

et al. 2021

Remote Sensing

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Land use/land cover (LULC) change has been recognized as one of the most important indicators to study ecological and environmental changes. Remote sensing provides an effective way to map and monitor LULC change in real time and for large areas. However, with the increasing spatial resolution of remote sensing imagery, traditional classification approaches cannot fully represent the spectral and spatial information from objects and thus have limitations in classification results, such as the “salt and pepper” effect. Nowadays, the deep semantic segmentation methods have shown great potential to solve this challenge. In this study, we developed an adaptive band attention (BA) deep learning model based on U-Net to classify the LULC in the Three Gorges Reservoir Area (TGRA) combining RapidEye imagery and topographic information. The BA module adaptively weighted input bands in convolution layers to address the different importance of the bands. By comparing the performance of our model with two typical traditional pixel-based methods including classification and regression tree (CART) and random forest (RF), we found a higher overall accuracy (OA) and a higher Intersection over Union (IoU) for all classification categories using our model. The OA and mean IoU of our model were 0.77 and 0.60, respectively, with the BA module and were 0.75 and 0.58, respectively, without the BA module. The OA and mean IoU of CART and RF were both below 0.51 and 0.30, respectively, although RF slightly outperformed CART. Our model also showed a reasonable classification accuracy in independent areas well outside the training area, which indicates the strong model generalizability in the spatial domain. This study demonstrates the novelty of our proposed model for large-scale LULC mapping using high-resolution remote sensing data, which well overcomes the limitations of traditional classification approaches and suggests the consideration of band weighting in convolution layers.

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<title>Automated identification of endmembers from hyperspectral data using mathematical morphology</title>

2002

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One of the most widely used approaches to analyze hyperspectral data is pixel unmixing, which relies on the identification of the purest spectra from the data cube. Once these elements, known as "endmembers", are extracted, several methods can be used to map their spatial distributions, associations and abundances. A large variety of methodologies have been recently proposed with the purpose of extracting endmembers from hyperspectral data. Nevertheless, most of them only rely on the spectral response; spatial information has not been fully exploited yet, specially in unsupervised classification. The integration of both spatial and spectral information is becoming more relevant as the sensors tend to increase their spatial/spectral resolution. Mathematical morphology is a non-linear image analysis and pattern recognition technique that has proved to be especially well suited to segment images with irregular and complex shapes, but has rarely been applied to the classification/segmentation of multivariate remote sensing data. In this paper we propose a completely automated method, based on mathematical morphology, which allows us to integrate spectral and spatial information in the analysis of hyperspectral images. The accuracy of the proposed algorithm is tested by its application to real hyperspectral data, and the results provided are compared to those found using other existing endmember extraction algorithms.

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<title>Integration of spatial and spectral information in unsupervised classification for multispectral and hyperspectral data</title>

Cited by 6 publications

References 0 publications

Spatial/spectral endmember extraction by multidimensional morphological operations

Spatial/spectral endmember extraction by multidimensional morphological operations

Land Use and Land Cover Mapping Using RapidEye Imagery Based on a Novel Band Attention Deep Learning Method in the Three Gorges Reservoir Area

<title>Automated identification of endmembers from hyperspectral data using mathematical morphology</title>

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