An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

Han, Youkyung; Chang, Anjin; Choi, Seokheun; Park, Honglyun; Choi, Jaewon

doi:10.1155/2017/9702612

Cited by 7 publications

(6 citation statements)

References 46 publications

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“…Liu et al [24] describe a hierarchical CD approach, aiming at distinguishing all the possible change types by considering discriminable spectral behaviors. Based on statistical analyses of spectral profiles, Han et al [25] adopt an unsupervised CD algorithm for denoising without dimensionality reduction.…”

Section: Related Workmentioning

confidence: 99%

GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

Wang

Yuan

et al. 2019

IEEE Trans. Geosci. Remote Sensing

480

226

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Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with high spectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD). The main contributions of this work are threefold: 1) Mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multi-source information; 2) 2-D CNN is designed to learn the discriminative features effectively from multi-source data at a higher level and enhance the generalization ability of the proposed CD algorithm; 3) A new HSI-CD data set is designed for objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate the proposed method outperforms most of the state-of-the-arts.

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Section: Related Workmentioning

confidence: 99%

GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

Wang

Yuan

et al. 2019

IEEE Trans. Geosci. Remote Sensing

480

226

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“…This method has recently been supplemented with synthetic fusion of images [15] called SFI-IRMAD which has shown to give a boost to the change detection performance. Another recent method showed promising results for change detection in hyperspectral images using synthetic fusion of images [16]. In this case, they have used a custom difference operator that is mainly based on the spectral angle mapper (SAM) [17].…”

Section: Literature Surveymentioning

confidence: 99%

“…For comparison, Window-based PCA+kmeans [13], IRMAD [14], SFI-IRMAD [15], SFI-DSP [16] and DSFANet [6] are used. S3DCAE [9] is used for comparison as the deep features extracted by it could be used for unsupervised change detection too.…”

Section: Comparison Of Modelsmentioning

confidence: 99%

Unsupervised Change Detection in Hyperspectral Images using Feature Fusion Deep Convolutional Autoencoders

Chakraborty,

Ghosh

2021

Preprint

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Binary change detection in bi-temporal co-registered hyperspectral images is a challenging task due to the large number of spectral bands present in the data. Researchers, therefore, try to handle it by reducing dimensions. The proposed work aims to build a novel feature extraction system using a feature fusion deep convolutional autoencoder for detecting changes between a pair of such bi-temporal co-registered hyperspectral images. The feature fusion considers features across successive levels and multiple receptive fields and therefore adds a competitive edge over the existing feature extraction methods. The change detection technique described is completely unsupervised and is much more elegant than other supervised or semi-supervised methods which require some amount of label information. Different methods have been applied on the extracted features to find the changes in the two images and it is found that the proposed method clearly outperformed the state of the art methods in unsupervised change detection for all the datasets.

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“…The advantage of that particular method is its ability to generate a change map obtained from two different sensors [15]. Nevertheless, the CD accuracy depends on the classification accuracy [23].Additionally, CD can be implemented in a tensor factorization method [25], a semi-supervised method [3], and an unsupervised statistical analysis method [26]. The multi-temporal images are analyzed using a three-dimensional (3D) tensor cube [25], and a higher-order orthogonal iteration algorithm is used to detect the changes.…”

mentioning

confidence: 99%

“…Additionally, CD can be implemented in a tensor factorization method [25], a semi-supervised method [3], and an unsupervised statistical analysis method [26]. The multi-temporal images are analyzed using a three-dimensional (3D) tensor cube [25], and a higher-order orthogonal iteration algorithm is used to detect the changes.…”

mentioning

confidence: 99%

Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

et al. 2018

Self Cite

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Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral-spatial and temporal modules. Particularly, a spectral-spatial module with a 3D convolutional layer extracts the spectral-spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral-spatial-temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM. from various platforms, such as aircraft, satellites, and unmanned aerial vehicles [1]. The spectral profiles obtained from HSIs help to achieve target detection, classification, as well as change detection (CD) because of the profiles ability to distinguish the spectrally similar materials and describe the finer spectral changes [2,3].CD is the process of identifying changes in land cover or land use that have occurred over time in the same geographical area [4]. Applications of CD techniques include assessing natural disasters, monitoring crops, and managing water resources [5][6][7]. Moreover, CD has offered the benefit of hyperspectral remote sensing over the past two decades. As HSIs offer more-detailed information about spectral changes than multispectral images, HSIs can improve the performance of CD [8]. Ho...

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An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

Cited by 7 publications

References 46 publications

GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

Unsupervised Change Detection in Hyperspectral Images using Feature Fusion Deep Convolutional Autoencoders

Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

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