Mapping of crop rotation using multidate Indian Remote Sensing Satellite digital data

Panigrahy, Sushma; Sharma, Shruti

doi:10.1016/s0924-2716(97)83003-1

Cited by 95 publications

(46 citation statements)

References 3 publications

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“…The use of remote sensing has multiple advantages and applications, and one of the key applications amongst them is the crop classification; i.e. differentiating between different varieties of crops [1].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, they can also be used to make an early estimate of the crop yield. Further, based on the available information, activities like -deciding type of crop and its acreage [3], determining the growth stage of crop [4], delineating their extent [1] can be planned in advance. All such information can be used in the overall improvement of the agricultural yield.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

Senthilnath

Kulkarni

Benediktsson

et al. 2016

IEEE Geosci. Remote Sensing Lett.

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Abstract-Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other natureinspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

Senthilnath

Kulkarni

Benediktsson

et al. 2016

IEEE Geosci. Remote Sensing Lett.

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“…Localised catastrophes could be verified, and variance-covariance analyzed components. In prior investigations, NDVI has a moderate-to-high accuracy in forecasting crop production [3][4][5][6][7][8][9][10][11], but boundary areas (transition from drought to normal conditions) have not been analyzed. In the present study, the NDVI is stable in the transition zone, and strong enough to detect statistically significant differences in plant growth (irrigated vs. non-irrigated), even early in the plant growth cycle (Figure 3).…”

Section: Discussionmentioning

confidence: 99%

Remote sensing and US crop insurance program integrity: data mining satellite and agricultural data

Little¹,

Schucking²,

Gartrell³

et al. 2007

WIT Transactions on Information and Communication Technologies, Vol 38

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The objective of this investigation is to (1) integrate remote sensing data into an existing data warehouse of the US crop insurance program 1990 to 2007, (2) test remote sensing correlations with crop production, and (3) use remotely sensed time series data to assess variation in crop production. Previously (2000 to 2007) data mining of the data warehouse was based upon probabilistic and algorithmic approaches to identification of possible fraud, waste, or abuse. The value of adding satellite data warehouse to data mining resources is provision of (1) an additional empirical metric, (2) objective data on vegetative health, (3) measurable metrics for capturing the variance of plant health, and (4) a means for measuring the covariance of location and production. Refinement of data mining through the addition of satellite data for routine use to reduce fraud, waste, and abuse will ultimately diminish the frequency of false positives.

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“…Considering Panax notoginseng is a vulnerable crop which has a serious succession cropping obstacle [50], consequently the continuous planting of the same crop in the same field will lead to the decrease of yield and quality. In order to promote the quality of production, it is crucial to monitor the spatial planting patterns of Panax notoginseng fields, such as crop rotation [51,52]. The planting pattern implies standardized planting with the specific crop structure and spatio-temporal configuration in the same field for a specific region under the particular natural resource and socio-economic conditions [53,54] so as to realize the sustainable utilization of agricultural resources and crop yield.…”

Section: Introductionmentioning

confidence: 99%

Single-Class Data Descriptors for Mapping Panax notoginseng through P-Learning

Deng

2018

Applied Sciences

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Machine learning-based remote-sensing techniques have been widely used for the production of specific land cover maps at a fine scale. P-learning is a collection of machine learning techniques for training the class descriptors on the positive samples only. Panax notoginseng is a rare medicinal plant, which also has been a highly regarded traditional Chinese medicine resource in China for hundreds of years. Until now, Panax notoginseng has scarcely been observed and monitored from space. Remote sensing of natural resources provides us new insights into the resource inventory of Chinese materia medica resources, particularly of Panax notoginseng. Generally, land-cover mapping involves focusing on a number of landscape classes. However, sometimes a subset or one of the classes will be the only part of interest. In term of this study, the Panax notoginseng field is the right unit class. Such a situation makes single-class data descriptors (SCDDs) especially significant for specific land-cover interpretation. In this paper, we delineated the application such that a stack of SCDDs were trained for remote-sensing mapping of Panax notoginseng fields through P-learning. We employed and compared SCDDs, i.e., the simple Gaussian target distribution, the robust Gaussian target distribution, the minimum covariance determinant Gaussian, the mixture of Gaussian, the auto-encoder neural network, the k-means clustering, the self-organizing map, the minimum spanning tree, the k-nearest neighbor, the incremental support vector data description, the Parzen density estimator, and the principal component analysis; as well as three ensemble classifiers, i.e., the mean, median, and voting combiners. Experiments demonstrate that most SCDDs could achieve promising classification performance. Furthermore, this work utilized a set of the elaborate samples manually collected at a pixel-level by experts, which was intended to be a benchmark dataset for the future work. The measuring performance of SCDDs gives us challenging insights to define the selection criteria and scoring proof for choosing a fine SCDD in mapping a specific landscape class. With the increment of remotely sensed satellite data of the study area, the spatial distribution of Panax notoginseng could be continuously derived in the local area on the basis of SCDDs.

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Mapping of crop rotation using multidate Indian Remote Sensing Satellite digital data

Cited by 95 publications

References 3 publications

A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

Remote sensing and US crop insurance program integrity: data mining satellite and agricultural data

Single-Class Data Descriptors for Mapping Panax notoginseng through P-Learning

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