Change Detection in Multitemporal Hyperspectral Images

Bruzzone, Lorenzo; Bovolo, Francesca; Du, Peijun

doi:10.1007/978-3-319-47037-5_4

Cited by 21 publications

(7 citation statements)

References 43 publications

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“…However, relatively few works covering CD in HSIs can be found. Recently, a book chapter [284] analyzed this challenging task and provided a literature review and comparison between the CD in multispectral and hyperspectral images. Problems and challenges in the existing methods were also discussed.…”

Section: Change Detectionmentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

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Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information, which make a plethora of applications for the analysis of large areas of the Earth surface feasible. However, a huge number of factors, such as high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions, make such data inherently nonlinear and complex, which poses extreme challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide. This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality reduction, resolution enhancement, hyperspectral image denoising and restoration, change detection, and fast computing. For each topic, we provide a synopsis of the state-of-the-art approaches and numerical results for validation and evaluation of different methodologies, followed by a discussion of future challenges and research directions.

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Section: Change Detectionmentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

Self Cite

670

266

View full text Add to dashboard Cite

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“…The first hyperspectral dataset is made of a pair of real bi-temporal hyperspectral images, collected from an irrigated agricultural field in Hermiston City in Umatilla County, Oregon, United States, which were acquired by the Hyperion sensor mounted onboard the Earth Observing-1 (EO-1) satellite May 1, 2004 andMay 8, 2007, respectively [40], [41]. The Hermiston images have the size of 390 × 200 pixels, and 242 spectral bands ranging from 0.4-2.5 µm.…”

Section: A Hyperspectral Datasetsmentioning

confidence: 99%

“…These images have a spectral resolution of 10 nm and a spatial resolution of 30 m. The main change on these datasets is farmland land-cover change, including the transitions among crops, soil, water and other land-cover types. The change occurred in the cropland is mainly due to vegetation water content affected by irrigation condition, and to the difference of crop growth situation [41]. The scene and the ground-truth map are shown in Fig.…”

Section: A Hyperspectral Datasetsmentioning

confidence: 99%

Three-Order Tucker Decomposition and Reconstruction Detector for Unsupervised Hyperspectral Change Detection

Hou

Tao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Change detection from multi-temporal hyperspectral images has attracted great attention. Most of traditional methods using spectral information for change detection treat a hyperspectral image as a 2D matrix, and do not take into account inherently structure information of spectrum, which leads to limited detection accuracy. To better approximate both spectral and spatial information, a novel three-order Tucker decomposition and reconstruction detector (TDRD) is proposed for hyperspectral change detection. Initially, Tucker decomposition and reconstruction strategies are used to eliminate influence of various factors in a multi-temporal dataset. Specifically, a singular value accumulation strategy is used to determine principal components in factor matrixs. Meanwhile, spectral angle is used to analyze spectral change after tensor processing in different domains. Finally, a new detector is designed to further improve the detection accuracy. Experiments conducted on five real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.

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“…The hyperspectral imagery provides more details from change detection (CD) compare to multispectral images . Hyperspectral change detection (HCD) is one of the most important applications of hyperspectral RS imagery (Bruzzone et al, 2016). The CD is the process of identifying differences in the state of an object or phenomenon by observing it at different times (Hussain et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Binary Hyperspectral Change Detection Based on 3d Convolution Deep Learning

Seydi

Hasanlou

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Timely and accurate change detection of Earth’s surface features is extremely important for understanding relationships and interactions between human and natural phenomena to promote better decision making. The bi-temporal hyperspectral imagery has a high potential for the detection of surface changes. However, the extraction of changes from bi-temporal hyperspectral imagery due to special content of data, and environment conditions (atmospheric condition), change into challenging task. To this end, this research proposed a change detection framework based on deep learning using bi-temporal hyperspectral imagery. The proposed framework is applied in two main steps: (1) predict phase that the change areas highlighted from no-change areas using image differencing algorithm (ID), (2) decision phase that it decides for detecting change pixels based on 3D convolution neural network (CNN). The efficiency of the presented method is evaluated using Hyperion multi-temporal hyperspectral imagery. To evaluate the performance of the proposed method, two bi-temporal hyperspectral Hyperion with a variety of land cover classes were used. The results show that the proposed method has high accuracy and low false alarms rate: overall accuracy is more than 95%, and the kappa coefficient is greater than 0.9 and the miss-detection is lower than 10% and the false rate is lower than 4%.

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Change Detection in Multitemporal Hyperspectral Images

Cited by 21 publications

References 43 publications

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Three-Order Tucker Decomposition and Reconstruction Detector for Unsupervised Hyperspectral Change Detection

Binary Hyperspectral Change Detection Based on 3d Convolution Deep Learning

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