Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

Renza, Diego; Martínez, Elvira; Molina, Íñigo; Ballesteros, Miguel

doi:10.1016/j.asr.2017.01.027

Cited by 36 publications

(25 citation statements)

References 24 publications

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“…These distances calculate the angle between each pixel of the images, considering each pixel as a vector in a space whose dimension is the number of bands of the image. Both metrics have been used for classification and clustering [12,19], as well as for change detection [7,12]. Their difference lies in the fact that SCM standardizes the data, centralizing it in the mean of the spectra of the two images.…”

Section: Spectral Similarity Indicesmentioning

confidence: 99%

“…In addition, the SAM index, originally proposed to evaluate the quality of a fused product, was recently used as a comparison index in a change detection approach [7].…”

Section: Spectral Similarity Indicesmentioning

confidence: 99%

“…The error matrices are obtained by means of the comparison between the change map resulting from each one of the unsupervised methods and a reference, given by the change map obtained with the supervised method. The accuracy of the change detection methods is measured through Overall Accuracy (OA) and Kappa (κ) indices, where OA is the ratio between pixels correctly classified and the total number of pixels, while κ index measures statistically the concordance between the maps cited above [7].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…This is an advantage of the image pixel as the unit of analysis, with regard to techniques such as kernelbased methods, where the computational cost can be an inconvenient in some cases [7]. On the other hand, an object is a group of pixels which includes context information and that typically involves the following steps: image segmentation; object extraction and comparison; and classification [1].…”

Section: Introductionmentioning

confidence: 99%

“…The Spectral Angle Mapper (SAM) and the Spectral Correlation Mapper (SCM) were proposed in [12] to measure the similarity between two spectral feature vectors applied to change detection. Additionally, in [7], the angle between each multitemporal image and a reference vector measured by means of SAM metric was proposed as a solution for change detection in specific land covers.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of spectral similarity indices in unsupervised change detection approaches

Ramos¹,

Renza²,

Ballesteros³

2018

DYNA

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Unsupervised change detection (UCD) is a subject of Remote Sensing whose objective is to detect the differences between two multi-temporal images. In some cases, spectral similarity indices have been used as the comparison block in algorithms of UCD. The aim of this paper is to show in a quantitative way the performance of four spectral similarity indices in the correct identification of changes. Comparison is performed in terms of precision (overall accuracy and kappa index) over medium and high-resolution images (SPOT-5: Satellite Pour l'Observation de la Terre and Quickbird), with a reference obtained through a post-classification method (based on Support Vector Machines, SVM). The results show dependence on the automatic thresholding technique, as well as on the classes associated with the change.Keywords: change detection; spectral indices; remote sensing; accuracy assessment.Evaluación de índices de similitud espectral en esquemas de detección de cambios no supervisados Resumen La detección de cambios de forma no-supervisada (UCD) es un área de teledetección, cuyo objetivo consiste en encontrar las diferencias entre dos imágenes multi-temporales. En algunos casos, los índices de similitud espectral son utilizados como bloque de comparación de UCD. El objetivo de este documento consiste en analizar de forma cuantitativa el desempeño de cuatro índices de similitud espectral en la correcta identificación de cambios. La evaluación se realiza en términos de la precisión (mediante la precisión global e índice kappa) utilizando imágenes de media y alta resolución (SPOT-5: Satélite Para la Observación de la Tierra y Quickbird), así como una imagen de cambio de referencia obtenida a través de un método de post-clasificación (basado en Máquinas de Soporte Vectorial, SVM). Los resultados obtenidos presentan dependencia con la técnica automática de umbralización, así como con las clases asociadas con el cambio.Palabras clave: detección de cambios; índices espectrales; teledetección; evaluación de precisión.

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Section: Spectral Similarity Indicesmentioning

confidence: 99%

“…In addition, the SAM index, originally proposed to evaluate the quality of a fused product, was recently used as a comparison index in a change detection approach [7].…”

Section: Spectral Similarity Indicesmentioning

confidence: 99%

Section: Accuracy Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of spectral similarity indices in unsupervised change detection approaches

Ramos¹,

Renza²,

Ballesteros³

2018

DYNA

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Land cover mapping based on time‐series MODIS‐NDVI using a dynamic time warping approach: A casestudy of the agricultural pastoral ecotone of northern China

et al. 2020

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To curb land degradation, a series of ecological restoration projects have been carried out since 1999, leading to dramatic land cover change (LCC) in the agricultural pastoral ecotone of northern China (APENC). To date, there is still lack of timely and accurate land cover (LC) information for management and assessment actions. This paper presents a LC mapping scheme to map annual LC information based on dynamic time warping (DTW) approach and time‐series MODIS‐NDVI product for the APENC. The DTW approach was optimized firstly based on the change of vegetation phenology phase. Next, typical reference time‐series curves set of different LC types were established and pixel‐wise similarity was examined to identify the LC type. Before that, the reconstruction of time‐series MODIS‐NDVI was performed using harmonic analysis of time‐series and reconstruction accuracy was evaluated quantitatively at a regional scale. Results showed that the gap error was relatively large for the high vegetation cover areas while it might be ignored for the moderate and low vegetation cover areas. The overall accuracy of LC mapping in year 2010 (LC‐2010) was 77.89% and kappa coefficient reached 0.71, which suggested that LC mapping scheme could extract LC information with high accuracy. The producer's accuracy ranged between 69.83% (grasslands) and 100% (water body), while the user's accuracy changed from 58% (built‐up) to 86% (forest). The overall spatial agreement between LC‐2010 and GlobaLand30‐2010 was about 64.44%. The total area of forests, croplands, water body and built‐up exhibited an increasing trend while the contrary trend were found for the grasslands and bare land during the 2001–2017. These LC maps are valuable for research the land degradation investigation and monitoring, benefit evaluation of ecological restoration projects and effect of LCC on surface hydrothermal process in APENC.

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A two-stage framework of target detection in high-resolution hyperspectral images

Xia

et al. 2019

SIViP

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Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

Cited by 36 publications

References 24 publications

Evaluation of spectral similarity indices in unsupervised change detection approaches

Evaluation of spectral similarity indices in unsupervised change detection approaches

Land cover mapping based on time‐series MODIS‐NDVI using a dynamic time warping approach: A casestudy of the agricultural pastoral ecotone of northern China

A two-stage framework of target detection in high-resolution hyperspectral images

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