Unsupervised Scale-Driven Change Detection With Deep Spatial–Spectral Features for VHR Images

Zhan, Tao; Gong, Maoguo; Jiang, Xiangming; Zhang, Mingyang

doi:10.1109/tgrs.2020.2968098

Cited by 38 publications

(17 citation statements)

References 58 publications

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“…For example, one could train only based on pseudo labels with high certainty and discard uncertain data points. Similarly, in some unsupervised methods such as MSDRL for VHR imagery, an initial pseudo-classification is separated by confidence where high confidence examples are used for training a classifier that subsequently obtains predictions for uncertain pixels [20]. In these methods, SiROC could also be used to obtain initial predictions and uncertainties to potentially improve not only the initial classification but maybe also the uncertainty categorization.…”

Section: Discussionmentioning

confidence: 99%

“…DCVA has also been combined with selfsupervised pre-training of the feature extractor specifically for remote sensing images [19]. MSDRL [20] is a scale-driven unsupervised method that uses deep feature extraction to obtain a pseudo-classification of change superpixels. Superpixels with high certainty pseudo-labels are then taken as input to train a support vector machine which eventually classifies the uncertain superpixels.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images

Kondmann

Toker

Saha

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Detecting changes on the ground in multitemporal Earth observation data is one of the key problems in remote sensing. In this paper, we introduce Sibling Regression for Optical Change detection (SiROC), an unsupervised method for change detection in optical satellite images with medium and high resolution. SiROC is a spatial context-based method that models a pixel as a linear combination of its distant neighbors.It uses this model to analyze differences in the pixel and its spatial context-based predictions in subsequent time periods for change detection. We combine this spatial context-based change detection with ensembling over mutually exclusive neighborhoods and transitioning from pixel to object-level changes with morphological operations. SiROC achieves competitive performance for change detection with medium-resolution Sentinel-2 and highresolution Planetscope imagery on four datasets. Besides accurate predictions without the need for training, SiROC also provides a well-calibrated uncertainty of its predictions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images

Kondmann

Toker

Saha

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…Recently, CNN converts to the most popular methods in a field on image processing (Alom et al, 2019;Ball et al, 2017). This method has shown promising performance in many applications such as classification (He et al, 2017), target detection (Hao et al, 2020;Vincent and Besson, 2020), damage detection (Seydi and Rastiveis, 2019), and change detection (Ghosh and Chakravortty, 2020;Huang et al, 2019;Ma et al, 2019;Wang et al, 2019;Zhan et al, 2020). The 2D-CNN focuses on the extraction of HSI spatial information and missing channel-related information (He et al, 2017;Li et al, 2017;Song et al, 2018).…”

Section: D Conventional Neural Networkmentioning

confidence: 99%

“…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). The change detection is used in many applications that include: urban monitoring, land use/cover mapping, and damage assessment (Ghosh and Chakravortty, 2020;Liu et al, 2019;López-Fandiño et al, 2019;Pati et al, 2020;Zhan et al, 2020). Recently, due to the increasing availability of hyperspectral images the HCD convert to hot research topic area and many methods have been developed by researchers.…”

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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“…(2) However, only using spectral features is bound to ignore spatial contextual information [12]. Therefore, joint spatial-spectral analysis is a common technical means in HSI-based tasks [13][14][15][16][17]. Therefore, the other is to obtain changes and improve detection accuracy through joint analysis of spectral and spatial features of HSI.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images

et al. 2021

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Joint analysis of spatial and spectral features has always been an important method for change detection in hyperspectral images. However, many existing methods cannot extract effective spatial features from the data itself. Moreover, when combining spatial and spectral features, a rough uniform global combination ratio is usually required. To address these problems, in this paper, we propose a novel attention-based spatial and spectral network with PCA-guided self-supervised feature extraction mechanism to detect changes in hyperspectral images. The whole framework is divided into two steps. First, a self-supervised mapping from each patch of the difference map to the principal components of the central pixel of each patch is established. By using the multi-layer convolutional neural network, the main spatial features of differences can be extracted. In the second step, the attention mechanism is introduced. Specifically, the weighting factor between the spatial and spectral features of each pixel is adaptively calculated from the concatenated spatial and spectral features. Then, the calculated factor is applied proportionally to the corresponding features. Finally, by the joint analysis of the weighted spatial and spectral features, the change status of pixels in different positions can be obtained. Experimental results on several real hyperspectral change detection data sets show the effectiveness and advancement of the proposed method.

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Unsupervised Scale-Driven Change Detection With Deep Spatial–Spectral Features for VHR Images

Cited by 38 publications

References 58 publications

Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images

Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images

Binary Hyperspectral Change Detection Based on 3d Convolution Deep Learning

Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images

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