Hyperspectral crop classification using fusion of spectral, spatial features and vegetation indices: Approach to the big data challenge

Reshma, S.; Veni, S.; George, Jeena Elsa

doi:10.1109/icacci.2017.8125870

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…There are two challenges for crop classification and identification due to the spectral similarity and the huge size of the input data. The authors in [18] proposed crop classification technique which combine various features (spectral, spatial and vegetation index features) to address the spectral similarity challenge for Big data in agriculture. Their technique involves dimensionality reduction using PCA (principal component analysis), MNF (minimum noise transform) in the first stage, followed by the support vector machine (SVM) supervised classification.…”

Section: B Big Data With Hyperspectral Analytics In Agriculturementioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.

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Section: B Big Data With Hyperspectral Analytics In Agriculturementioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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“…Since there is a huge amount of information present in a scene and small number of samples available Hyperspectral Image Classification becomes a daunting task. This issue is addressed by reducing the complexity of the data set using feature extraction [2].…”

Section: Introductionmentioning

confidence: 99%

Analysing Effect of t-SNE and 1-D CNN on Performance of Hyperspectral Image Classification

Hariharan¹

2021

TURCOMAT

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Feature extraction is a crucial step in Hyperspectral Image classification that aids in processing data effectively without losing relevant information. This step is essential when dealing with images with high dimensions because they suffer from Hughes phenomenon or the curse of high dimensionality. This phenomenon occurs in high dimensional datasets where the number of training samples is limited. In this paper, we have studied the influence of feature extraction techniques in HSI classification. We have compared the efficiency of three widely used techniques, namely Principal Component Analysis, t- Stochastic Neighbor Embedding and Convolutional Neural Network. Overall classification accuracy for PCA when used with KNN, a commonly used classification algorithm was found to be 69.79% while t-SNE with KNN was 71.04%. When CNN was used for feature extraction, its outperformed t-SNE and PCA with a wide margin with classification accuracy reaching as high as 95.06%.

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“…The results from Zhang et al [34] showed that combining spectral and textural features for classification achieved the highest overall accuracy of 98.65%. Reshma et al [36] raised the classification accuracy to 98.07% by integrating the vegetation indices along with the spectral and spatial features for classification. The studies mentioned above indicated that the combination of spectral and spatial features was useful for the identification of various crop types.…”

Section: Introductionmentioning

confidence: 99%

Crop Area Identification Based on Time Series EVI2 and Sparse Representation Approach: A Case Study in Shandong Province, China

et al. 2019

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The accurate and timely spatial distribution information of various crop types is vital for food security. In this study, the 2-band enhanced vegetation index (EVI2) data from Moderate Resolution Imaging Spectroradiometer (MODIS) were combined with sparse representation approach to identify the distribution of various crop types in Shandong Province, China. Three groups of input variables derived from EVI2 including annual time series EVI2 (TS), harmonic features (HF), and combined vector formed by harmonic features and texture features (HFT) were used. The online dictionary learning and orthogonal matching pursuit algorithms were applied to generate the dictionary and solve the sparse coefficients of the identified samples, respectively. Then, the label of the identified samples can be obtained according to the minimum residuals between the dictionary and the sparse coefficients of the identified samples. At the provincial level, the validation based on the statistical data showed that three groups of input variables presented lower than ±25% at relative errors, and input variables of HFT performed better than the other two. At the municipal level, the results achieved by using input variables of HFT also agreed well with the statistics with the coefficient of determination R 2 > 0.85 for wheat and maize, as well as R 2 > 0.71 for peanut and cotton during 2014-2016. These results demonstrate that the combination of the input variables of HFT derived from time series MODIS EVI2 data and sparse representation approach can be used for crop identification in the study area. INDEX TERMS Crop area identification, harmonic features, sparse representation, texture features, time series EVI2.

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Hyperspectral crop classification using fusion of spectral, spatial features and vegetation indices: Approach to the big data challenge

Cited by 7 publications

References 16 publications

Big Data and Machine Learning With Hyperspectral Information in Agriculture

Big Data and Machine Learning With Hyperspectral Information in Agriculture

Analysing Effect of t-SNE and 1-D CNN on Performance of Hyperspectral Image Classification

Crop Area Identification Based on Time Series EVI2 and Sparse Representation Approach: A Case Study in Shandong Province, China

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