An Image-Based Framework for Ocean Feature Detection and Analysis

Banesh, Divya; Petersen, Mark; Ahrens, James; Turton, Terece L.; Samsel, Francesca; Schoonover, Joseph; Hamann, Bernd

doi:10.1007/s41651-021-00085-8

Cited by 7 publications

(2 citation statements)

References 64 publications

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“…The effectiveness of the proposed change detection approaches was quantitatively compared and analyzed based on four metrics [33][34][35]:1) the false alarm rate (FA), which represents the percentage of pixels incorrectly classified into changed regions; 2) the missed detection rate (MD), which represents the percentage of pixels incorrectly classified into unchanged regions; 3) the overall accuracy (OA), which is the ratio of the total number of correctly detected pixels to the total number of pixels; and 4) the Kappa coefficients.…”

Section: B Evaluation Criteriamentioning

confidence: 99%

Remote-Sensing-Based Change Detection Using Change Vector Analysis in Posterior Probability Space: A Context-Sensitive Bayesian Network Approach

Song

et al. 2023

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

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Change vector analysis (CVA) and post-classification change detection (PCC) have been the most widely used changedetection methods. However, CVA requires sound radiometric correction to achieve optimal performance, and PCC is susceptible to accumulated classification errors. Although change vector analysis in the posterior probability space (CVAPS) was developed to resolve the limitations of PCC and CVA, the uncertainty of remote sensing imagery limits the performance of CVAPS owing to three major problems: 1) mixed pixels, 2) identical ground cover type with different spectra, and 3) different ground cover types with the same spectrum. To address this problem, this study proposes the FCM-CSBN-CVAPS approach under the CVAPS framework. The proposed approach decomposes the mixed pixels in to m u l t i p l e s ig n a l c la s s e s u s in g th e fu z z y C m e a n s ( F C M ) algorithm. Although the mixed pixel problem is less severe in the high-resolution image, the change detection performance is still enhanced because, as a soft clustering algorithm, FCM is less susceptible to cumulative clustering error. Then, a contextsensitive Bayesian network (CSBN) is constructed to establish multiple-to-multiple stochastic linkages between signal pairs and ground cover types by incorporating spatial information to resolve problems 2 and 3 discussed above. Finally, change detection is performed using CVAPS in the posterior probability space. The effectiveness of the proposed approach is evaluated on three bitemporal remote sensing datasets with different spatial sizes and resolutions. The experimental results confirm the effectiveness of FCM-CSBN-CVAPS in addressing the uncertainty problems of change detection and its superiority over other relevant changedetection techniques.

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Section: B Evaluation Criteriamentioning

confidence: 99%

Remote-Sensing-Based Change Detection Using Change Vector Analysis in Posterior Probability Space: A Context-Sensitive Bayesian Network Approach

Song

et al. 2023

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

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“…A vector map consists of a number of data layers, and each layer consists of attribute data and geometry data [25,26], as shown in Figure 1a. The attribute data presents the storage information such as ID, Text and Name.…”

Section: Vector Map Datamentioning

confidence: 99%

A Novel Invariant Based Commutative Encryption and Watermarking Algorithm for Vector Maps

Zhang

Wang

et al. 2021

IJGI

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Commutative encryption and watermarking (CEW) is an emerging method that combines encryption technology with digital watermarking technology. It has the dual capability of secure transmission and copyright protection. However, the existing CEW methods for vector maps have good robustness in resisting geometric attacks but poor resistance to vertex attacks (e.g., addition, deletion, etc.). To solve this problem, here we propose a novel invariant-based CEW algorithm for vector maps, which consists of permutation-based encryption scheme and coordinates-based watermarking scheme. In the encryption scheme, the encryption key is generated via the Gaussian distribution method combined with the SHA-512 hash method; then, the double random position permutation strategy is applied to the vector map encryption. In watermarking embedding scheme, the original watermark image is scrambled via logistic chaotic encryption before embedding, and the coordinates of all the vertices are normalized. Then, the scrambled watermark image is embedded into the normalized coordinates. Results show that: proposed method is more robust to conventional attacks (e.g., vertex addition and deletion, reordering and data format conversion) and geometric attacks (e.g., scaling and translation). In addition, compared with the existing CEW methods for vector maps, the proposed method has higher security and stronger robustness against vertex attacks.

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A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning

Che

Dai

et al. 2023

Big Earth Data

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An Image-Based Framework for Ocean Feature Detection and Analysis

Cited by 7 publications

References 64 publications

Remote-Sensing-Based Change Detection Using Change Vector Analysis in Posterior Probability Space: A Context-Sensitive Bayesian Network Approach

Remote-Sensing-Based Change Detection Using Change Vector Analysis in Posterior Probability Space: A Context-Sensitive Bayesian Network Approach

A Novel Invariant Based Commutative Encryption and Watermarking Algorithm for Vector Maps

A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning

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