A Superpixel-by-Superpixel Clustering Framework for Hyperspectral Change Detection

Li, Qiuxia; Mu, Tingkui; Gong, Hang; Dai, Haishan; Li, Chunlai; He, Zhiping; Wang, Wenjing; Han, Feng; Tuniyazi, Abudusalamu; Li, Haoyang; Lang, Xuechan; Li, Zhiyuan; Wang, Bin

doi:10.3390/rs14122838

Cited by 6 publications

(2 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhan et al [46] introduced a bilinear convolutional neural network integrated with the SLIC superpixel algorithm, aiming to reduce annotation requirements and enhance detection performance. Li et al [47] developed a Superpixelby-Superpixel Clustering Framework (SSCF) that employs both SLIC and the Gaussian Mixture Model (GMM) to refine the detection of subtle changes in Hyperspectral Image Change Detection (HSI-CD) and mitigate confusion surrounding varying degrees of change along decision boundaries. Furthermore, Zhang et al [48] formulated an Endto-End Superpixel Enhanced Change Detection Network (ESCNet), which amalgamates differentiable superpixel segmentation with deep convolutional neural networks, allowing for more precise localization of change regions in Very High-Resolution (VHR) images.…”

Section: B Superpixel Segmentation-based Change Detectionmentioning

confidence: 99%

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Zhang,

Liu,

Niu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Vector Polygons are valuable survey data, serving as crucial outputs of national geographical censuses and a fundamental data source for detecting changes in geographical conditions. Current remote-sensing image change detection methods rely on comparing images but overlook abundant historical vector results, struggle with model generalization, and lack adequate samples. Consequently, change detection remains a manual process primarily, unable to meet the requirements for automated and efficient monitoring of standardized geographical conditions. Hence, this paper proposes a change detection method for land cover vector polygons based on high-resolution remote sensing images and deep learning. Initially, the enhanced simple linear iterative clustering (SLIC) algorithm is applied to segment dual-temporal images from identical regions. Subsequently, an annotated dataset is generated using a multi-scale extraction, cropping-with-inpainting approach. Next, datasets derived from pre-and post-temporal images are used for training and testing, respectively, and the training set is purified by using twoclassifier cross-validation. Finally, an improved object-oriented convolutional neural network (CNN) model performs finegrained scene classification. The change rules and postprocessing method are then integrated to identify changed vector polygons. To validate the effectiveness and superiority of the proposed method, we conducted experiments on land cover change detection using datasets from two study areas. The results indicate that the proposed method achieves precision and recall rates of 91.89% and 94.44% on dataset-1, respectively. Similarly, in dataset-2, the precision and recall rates reach 87.59% and 91.41%, respectively. These findings demonstrate the method's efficacy in detecting changed vector polygons, reducing manual intervention, and enhancing detection efficiency.

show abstract

Section: B Superpixel Segmentation-based Change Detectionmentioning

confidence: 99%

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Zhang,

Liu,

Niu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Further, a CNN-based auto-encoder network is used to classify these hyperspectral remote sensing images. Li et al [32] used pixel-by-pixel clustering framework to represent the uncertainty in the pixels of hyperspectral remote sensing images. The model used deep CNN architecture with large number of weight parameters to classify hyperspectral remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Granulation-Based Convolutional Neural Networks With Single Pass Learning for Remote Sensing Image Classification

Pal

Kumar

2023

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

View full text Add to dashboard Cite

Convolutional neural networks (CNNs) with the characteristics like spatial filtering, feed-forward mechanism and back propagation-based learning are being widely used recently for remote sensing (RS) image classification. The fixed architecture of CNN with a large number of network parameters is managed by learning through a number of iterations, and thereby increasing the computational burden. To deal with this issue, an adaptive granulation-based CNN (AGCNN) model is proposed in the present study. AGCNN works in the framework of fuzzy set theoretic data granulation and adaptive learning by upgrading the network architecture to accommodate the information of new samples, and avoids iterative training, unlike conventional CNN. Here, granulation is done both on the 2-D input image and its 1-D representative feature vector output, as obtained after a series of convolution and pooling layers. While the class dependent fuzzy granulation on input image space exploits more domain knowledge for uncertainty modeling, rough set theoretic reducts computed on them selects only the relevant features for input to CNN. During classification of unknown patterns, a new principle of roughness-minimization with weighted membership is adopted on overlapping granules to deal with the ambiguous cases. All these together improve the classification accuracy of AGCNN, while reducing the computational time significantly. The superiority of AGCNN over some state-of-the-art models in terms of different performance metrics is demonstrated for hyperspectral(HS) and multispectral(MS) images both by quantitatively and visually.

show abstract

Progressive pseudo-label framework for unsupervised hyperspectral change detection

Li,

Mu,

Tuniyazi

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

A Superpixel-by-Superpixel Clustering Framework for Hyperspectral Change Detection

Cited by 6 publications

References 60 publications

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Adaptive Granulation-Based Convolutional Neural Networks With Single Pass Learning for Remote Sensing Image Classification

Progressive pseudo-label framework for unsupervised hyperspectral change detection

Contact Info

Product

Resources

About