An optimal remote sensing image enhancement with weak detail preservation in wavelet domain

Sharma, Rajni; Ravinder, M.; Sharma, Nitin; Sharma, Kanchan

doi:10.1007/s12652-021-02957-9

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Fuzzy C-means (FCM) based clustering performed image segmentation of remote sensing image only on the basis of their pixel gray level values [1,19,20,[32][33][34][35]. They do not involves the geographical or spatial information into account when segmenting the remote sensing images.…”

Section: Clustering-based Image Segmentationmentioning

confidence: 99%

“…The performance of the proposed FBFCM-ELM employed to carryout the remote sensing image classification is confirmed in terms of accuracy through comparison with five classifier models along with FBFCM feature extractor, viz. K-nearest neighbor (K-NN) [35], random forests (RF) [51], decision tree (DT) [52], ensemble voting classifier (EVC) [17], and support vector machine (SVM) [43]. In Table 7, the seven parameters are used for classification of remote sensing images are accuracy (OA), recall, precision, F1 score, MCC, Kappa values and AUC are presented.…”

Section: Evaluation Of the Proposed Fbfcm-elm Approachmentioning

confidence: 99%

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Remote sensing image segmentation using feature based fusion on FCM clustering algorithm

Sharma

Ravinder

2023

Complex Intell. Syst.

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Image segmentation of heterogeneous comparable objects lying beneath the earth’s surface is a fundamental but challenging research area in remote sensing. Learning approaches are used in remote sensing image segmentation to improve segmentation accuracy at the expense of time and a large amount of data, but their performance need to be finely classified due to information diversity constraints. In this work, we proposed an novel feature based fuzzy C-means-extreme learning machine (FBFCM-ELM) algorithm for remote sensing image segmentation in which the classification based on entropy, intensity, and edge features is performed in such a way that it updates the intensity value to preserve the most local characteristics in the image while still being able to clearly distinguish the image’s boundaries by assigning the pixel values of each cluster to the peak value of the cluster’s sub-histogram. Using FBFCM, features are extracted and used as reliable samples for ELM training. Undetermined segmented pixels are obtained using the trained ELM classifier. Experiments performed over number of images that confirmed the proposed method yields a better segmented RGB image, as evidenced by observable details, edges, and improved appearance that resembles the ground truth image and outperforms state-of-the-art algorithms.

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Section: Clustering-based Image Segmentationmentioning

confidence: 99%

Section: Evaluation Of the Proposed Fbfcm-elm Approachmentioning

confidence: 99%

Remote sensing image segmentation using feature based fusion on FCM clustering algorithm

Sharma

Ravinder

2023

Complex Intell. Syst.

Self Cite

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“…The method of manually correcting RSIs is labor-intensive, time-consuming and labor-intensive, and has great limitations in the face of massive RSIs. Therefore, instead, Python is used to automate and batch the pre-processing process, thus simplifying the time and effort of pre-processing RSI data [18].…”

Section: Automatic Pre-processing Of Rsi Satellite Datamentioning

confidence: 99%

Machine Learning Algorithm for Background Analysis of Remote Sensing Image Pollution Monitoring

2023

WPPCP

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Traditional water quality detection methods will consume a lot of manpower and material resources. However, remote sensing data and field water quality monitoring data are used to establish water quality remote sensing inversion model to achieve water quality remote sensing monitoring, which makes up for the shortcomings of traditional methods and can monitor the water quality environment comprehensively, quickly and dynamically. However, due to the impact of objective conditions, field water quality data is difficult to obtain in large quantities, so machine learning theory is used, A large number of easily obtained remote sensing image(RSI) data can effectively solve the problem of water quality monitoring. In this experiment, 50 polluted RSIs and 50 unpolluted RSIs are used to extract the gray distribution features of RSIs as the features of the training set. The parameters of GBDT model were optimized, and the accuracy of identifying polluted and unpolluted areas reached 98.67%. In addition, by comparing the performance of three machine learning algorithms, GBDT, DT and SVM, it is found that GBDT algorithm has the highest accuracy in RSI classification. The use of GBDT algorithm enables us to automatically monitor the pollution in RSIs without any marks, which realizes the automation of pollution monitoring.

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Enhancing infrared images via multi-resolution contrast stretching and adaptive multi-scale detail boosting

Liu

Pan

et al. 2023

Vis Comput

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An optimal remote sensing image enhancement with weak detail preservation in wavelet domain

Cited by 3 publications

References 21 publications

Remote sensing image segmentation using feature based fusion on FCM clustering algorithm

Remote sensing image segmentation using feature based fusion on FCM clustering algorithm

Machine Learning Algorithm for Background Analysis of Remote Sensing Image Pollution Monitoring

Enhancing infrared images via multi-resolution contrast stretching and adaptive multi-scale detail boosting

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