Classification of clouds in satellite imagery using over-complete dictionary via sparse representation

Jin, Wei; Wang, Lei; Zeng, Xingbin; Liu, Zhen; Fu, Randi

doi:10.1016/j.patrec.2014.07.015

Cited by 17 publications

(26 citation statements)

References 14 publications

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“…Constant C, as a penalty factor, which controls the trade-off between the volume and the errors in SVDD, is set to be C = 1.0. The classification accuracy of the proposed method is evaluated; several existing methods—including affinity based FSVM [14], SRC [16], and CCSI-ODSR [19]—are compared with the proposed method, the computation efficiency of the different methods is provided, and training time for training SVDD is listed in Section 5.4.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…In this experiment, AFSRC is compared with FSVM [14], SRC [16], and CCSI-ODSR [19]. As in the prior experiment, 200 samples of each cloud type are used for testing, and there are 1200 testing samples in total.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Due to the successful application of sparse representation for pattern classification problem [16,17,18], our research team proposed a cloud classification method using over-complete dictionary via sparse representation [19], which improved the classification accuracy in most weather systems, but this method did not take the fuzziness of cloud samples into consideration. By introducing the fuzziness into sparse representation, a cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed, this method defines some adaptive parameters which relate to the attenuation rate and critical value of fuzzy membership, then constructs an adaptive membership function to alleviate the problem that the traditional fuzzy membership function is ineffective in depicting the distribution characteristics of training samples.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

Jin

Gong

Zeng

et al. 2016

Sensors

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Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

Jin

Gong

Zeng

et al. 2016

Sensors

Self Cite

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“…The concept of sparse representation was first introduced by Huber in 1982 which is not proved fruitful at that time due computational timing cost [1]. As the technology advances and computational time reduces, this proves to be useful.…”

Section: Introductionmentioning

confidence: 99%

“…To build a sparsified dictionary which is based on a mathematical model obtained from the data using fixed basis dictionary composed of over-complete basis vectors. The other approach is to build an adaptive over-complete dictionary by learning a dictionary to perform better on a training set [1]. Sparse and redundant representation of signal modeling assumes an ability to represent signals as linear combinations of little atoms from a prespecified dictionary.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of optimization algorithms for sparse and redundant dictionaries

Qayyum

Malik

Nuafal

et al. 2015

2015 IEEE Student Symposium in Biomedical Engineering &Amp; Sciences (ISSBES)

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In this paper, we are comparing the optimization algorithms incorporated with overcomplete discrete cosine transform (DCT) based dictionary and discrete Tchebichef transform (DTT) basis function used as a dictionary for sparse representation. Further, we measured the performance of optimization greedy algorithm family e.g., Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP) and Stage-wise Orthogonal Matching Pursuit (St-OMP) and convex relaxation technique like Basis Pursuit (BP) on proposed pre-specified dictionaries in terms of accuracy and probability of error. We also compared performance in terms of sparse coefficients recovery atoms and analyzed the error of probability over small image patch using the fixed dictionaries. Result showed that our proposed dictionary based on DTT basis function performed well and also convex relaxation achieved good sparse coefficients as compared to other optimization algorithms. The BP produced more accuracy as compared to greedy techniques with little increase of the processing time.

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A novel fuzzy based deep neural network for rain fall prediction using cloud images

Manoj¹,

Harshavardhanan

Victor

et al. 2022

Concurrency and Computation

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Summary The most challenging aspect of weather forecasting is predicting rainfall. In contrast, timely and precise rainfall forecasting is immensely useful in developing effective security measures for active infrastructure projects, logistics, agricultural activities, air transport operations, and flood scenarios. Artificial intelligence has been established to predict rain precipitation, but very little research has been conducted on images than time‐series data to predict rainfall. However, the accuracy of the prediction model utilizing pictures is lower when compared to time series data. Thus a novel fuzzy based deep neural network (NFDNN) model was created to increase rainfall prediction performance. Initially, our research utilized input cloud images, in which the fuzzy wavelet incorporated with neural networks is utilized to identify clouds in the sky. Second, the types of clouds are determined by a fuzzy deep neural networks cloud classifier in which the adaptive fuzzy sparse representation is used. Then, the density‐based spatial clustering for applications containing noise clustering method was used to detect rainy clouds by evaluating the cloud's color and density from the fuzzy deep neural network cloud classifier. Finally, rainfall was predicted using information such as cloud categorization, appearance, and height obtained from cloud position and cloud types. As a result, the proposed NFDNN model gives a better prediction result than the existing methods.

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Classification of clouds in satellite imagery using over-complete dictionary via sparse representation

Cited by 17 publications

References 14 publications

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

Evaluation of optimization algorithms for sparse and redundant dictionaries

A novel fuzzy based deep neural network for rain fall prediction using cloud images

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