Importance of individual sample of training data in modified possibilistic c-means classifier for handling heterogeneity within a specific crop

Singhal, Mragank; Payal, Ashish; Kumar, Anil

doi:10.1117/1.jrs.15.034507

Cited by 11 publications

(16 citation statements)

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“…Castor crop is the intercrop with green gram, groundnut, and it produced a feasible yield. 1 Castor seed production showed an incremental rise from 11.97 lakh tones (2018-2019) to 20.60 lakh tones (2019-2020). During summer, farmers are going for seed production of castor crops.…”

Section: Introductionmentioning

confidence: 98%

“…Castor is widely cultivated in Gujarat, Tamil Nadu, Andhra Pradesh, and Rajasthan. 1 Crop monitoring and mapping are carried out using the spectral response of crops, which is based on phenology, morphology, and health status. 2 The multitemporal remote sensing data plays a major important role in crop classification and mapping.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the effect of the heterogeneity within the category, "individual sample as mean" approach was proposed for different statistical parameters in Fuzzy classification. 1 It reduces the effects of the heterogeneity within the category, while considering each sample as per similarity. "Individual sample as mean" approach could yield better results than Euclidean with "mean" and a few other classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…"Individual sample as mean" approach could yield better results than Euclidean with "mean" and a few other classification algorithms. 1 The ADMPLICM was used to remove the outlier using local neighbor pixels and to preserve the sides and limits respective to the classes. 2 The outliers and noise of respective data were reduced by its insensitive and robust characters.…”

Section: Introductionmentioning

confidence: 99%

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Handling heterogeneity within castor crop fields through “Individual Sample as Mean” training parameter approach

Murugan

Kumar

2023

J. Appl. Rem. Sens.

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.The high temporal resolution data has a great opportunity for specific crop mapping. The recent advancement in remote sensing data had a good impact on crop classification and mapping. In this research work, modified possibilistic c-means (MPCM) fuzzy machine learning approach has been applied for castor crop mapping, using sentinel 2A/2B satellite temporal images and local convolution as adaptive modified possibilistic local information c-means (ADMPLICM) algorithm was applied to remove isolated unwanted pixels. In training sample parameter “individual sample as mean” method was used to handle heterogeneity within the castor fields. The class-based sensor independent normalized difference vegetation index (CBSI-NDVI), normalized difference vegetation index (Red-NIR), and modified NDVI were produced, then separability analysis was conducted for the selecting optimum temporal date for castor crop mapping. The heterogeneity effect within the field was studied using variance parameters, and its values were observed low within the castor crop field. While considering the “individual sample as mean” training parameter approach on CBSI-NDVI, the variance of the target was decreased from 0.019 to 0.016. The identified castor field variance was close to the training field based on their high membership values and these fields had low mean membership difference (MMD) value. MMD values of castor training class with other crop classes were very high (0.425 – Isabgol, 0.502 – wheat, 0.259 – Fenugreek, 0.809 – Mustard, and 0.569 – Cumin), which indicates other crops were distinguished very well.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Handling heterogeneity within castor crop fields through “Individual Sample as Mean” training parameter approach

Murugan

Kumar

2023

J. Appl. Rem. Sens.

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“…Although the PCM algorithm is not as researched as FCM, numerous modified versions of PCM have come up recently to improve the clustering/classification performance of the conventional PCM algorithm to suit different applications [50][51][52][53][54][55][56]. In separate studies carried out by Ravindraiah and Chandra Mohan Reddy [55] and Chawla [56], the PCM algorithm was adapted to include spatial contextual information with spectral information for clustering/classification.…”

Section: Introductionmentioning

confidence: 99%

Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification

2021

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Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonetheless, the scope of integrating spatial contextual information with the conventional PCM algorithm, which has several advantages over the FCM algorithm for supervised classification, has not been explored much. This study proposed integrating local spatial information with the PCM algorithm using simpler but proven approaches from available FCM-based local spatial information algorithms. The three new PCM-based local spatial information algorithms: Possibilistic c-means with spatial constraints (PCM-S), possibilistic local information c-means (PLICM), and adaptive possibilistic local information c-means (ADPLICM) algorithms, were developed corresponding to the available fuzzy c-means with spatial constraints (FCM-S), fuzzy local information c-means (FLICM), and adaptive fuzzy local information c-means (ADFLICM) algorithms. Experiments were conducted to analyze and compare the FCM and PCM classifier variants for supervised LULC classifications in soft (fuzzy) mode. The quantitative assessment of the soft classification results from fuzzy error matrix (FERM) and root mean square error (RMSE) suggested that the new PCM-based local spatial information classifiers produced higher accuracies than the PCM, FCM, or its local spatial variants, in the presence of untrained classes and noise. The promising results from PCM-based local spatial information classifiers suggest that the PCM algorithm, which is known to be naturally robust to noise, when integrated with local spatial information, has the potential to result in more efficient classifiers capable of better handling ambiguities caused by spectral confusions in landscapes.

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Classifying Pinus roxburghii Using an Innovative Training Approach of Fuzzy Models While Handling Heterogeneity Within Class in Western Himalayan Forests

Mehrotra,

Kumar,

Roy

2024

J Indian Soc Remote Sens

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Importance of individual sample of training data in modified possibilistic c-means classifier for handling heterogeneity within a specific crop

Cited by 11 publications

References 0 publications

Handling heterogeneity within castor crop fields through “Individual Sample as Mean” training parameter approach

Handling heterogeneity within castor crop fields through “Individual Sample as Mean” training parameter approach

Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification

Classifying Pinus roxburghii Using an Innovative Training Approach of Fuzzy Models While Handling Heterogeneity Within Class in Western Himalayan Forests

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