A novel active learning approach for the classification of hyperspectral imagery using quasi-Newton multinomial logistic regression

Tan, Kun; Wang, Xue; Zhu, Jishuai; Hu, Jun; Zhang, Liangpei

doi:10.1080/01431161.2018.1433893

Cited by 15 publications

(5 citation statements)

References 48 publications

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“…In this study, two machine learning algorithms including RF and SVM were applied to crop classification. Recently, deep learning algorithms including convolutional neural network (CNN) were widely applied to remote sensing data classification [54][55][56]. Despite the promising performance of CNN, Kim et al [57] reported that the training sample size has greater effects on the accuracy of CNN than that of SVM in crop classification, indicating a need for numerous training samples for improved CNN classification performance.…”

Section: Classification Methodsmentioning

confidence: 99%

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Kwak

Park

2019

Applied Sciences

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Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often used for crop classification. From a data availability viewpoint, it is not always possible to acquire time-series UAV images due to limited accessibility to the study area. Thus, it is necessary to improve classification performance for situations when a single or minimum number of UAV images are available for crop classification. In this study, we investigate the potential of gray-level co-occurrence matrix (GLCM)-based texture information for crop classification with time-series UAV images and machine learning classifiers including random forest and support vector machine. In particular, the impact of combining texture and spectral information on the classification performance is evaluated for cases that use only one UAV image or multi-temporal images as input. A case study of crop classification in Anbandegi of Korea was conducted for the above comparisons. The best classification accuracy was achieved when multi-temporal UAV images which can fully account for the growth cycles of crops were combined with GLCM-based texture features. However, the impact of the utilization of texture information was not significant. In contrast, when one August UAV image was used for crop classification, the utilization of texture information significantly affected the classification performance. Classification using texture features extracted from GLCM with larger kernel size significantly improved classification accuracy, an improvement of 7.72%p in overall accuracy for the support vector machine classifier, compared with classification based solely on spectral information. These results indicate the usefulness of texture information for classification of ultra-high-spatial-resolution UAV images, particularly when acquisition of time-series UAV images is difficult and only one UAV image is used for crop classification.

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Section: Classification Methodsmentioning

confidence: 99%

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Kwak

Park

2019

Applied Sciences

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“…The novelty of M5 in terms of AL is its selective variance criterion. It has a parameter V, as described in [62]. In our experiment, this parameter was tuned and set as 0.1, which is consistent with the suggestion of the authors [62].…”

Section: Results Of Al Experimentsmentioning

confidence: 89%

“…It has a parameter V, as described in [62]. In our experiment, this parameter was tuned and set as 0.1, which is consistent with the suggestion of the authors [62]. M6 is specifically designed for object-based AL, and its work-flow is very similar to M3 since M6 also uses a breaking tie criterion.…”

Section: Results Of Al Experimentsmentioning

confidence: 96%

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Multi-Spectral Image Classification Based on an Object-Based Active Learning Approach

Zhang

Liu

2020

Remote Sensing

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In remote sensing, active learning (AL) is considered to be an effective solution to the problem of producing sufficient classification accuracy with a limited number of training samples. Though this field has been extensively studied, most papers exist in the pixel-based paradigm. In object-based image analysis (OBIA), AL has been comparatively less studied. This paper aims to propose a new AL method for selecting object-based samples. The proposed AL method solves the problem of how to identify the most informative segment-samples so that classification performance can be optimized. The advantage of this algorithm is that informativeness can be estimated by using various object-based features. The new approach has three key steps. First, a series of one-against-one binary random forest (RF) classifiers are initialized by using a small initial training set. This strategy allows for the estimation of the classification uncertainty in great detail. Second, each tested sample is processed by using the binary RFs, and a classification uncertainty value that can reflect informativeness is derived. Third, the samples with high uncertainty values are selected and then labeled by a supervisor. They are subsequently added into the training set, based on which the binary RFs are re-trained for the next iteration. The whole procedure is iterated until a stopping criterion is met. To validate the proposed method, three pairs of multi-spectral remote sensing images with different landscape patterns were used in this experiment. The results indicate that the proposed method can outperform other state-of-the-art AL methods. To be more specific, the highest overall accuracies for the three datasets were all obtained by using the proposed AL method, and the values were 88.32%, 85.77%, and 93.12% for “T1,” “T2,” and “T3,” respectively. Furthermore, since object-based features have a serious impact on the performance of AL, eight combinations of four feature types are investigated. The results show that the best feature combination is different for the three datasets due to the variation of the feature separability.

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“…In this section of the research, a survey is conducted on the HIS classification through the various method such as Machine learning, Extreme Machine Learning, Ensemble Machine Learning, Neural Network, SVM(Support Vector Machine), linear regression. In paper [9] an active learning approach were proposed for the HSI classification using the Powell selective variance and Q-N multinomial LR (logistic regression). Here the approach had dual steps , first step deals with the fast approach for the MLR classification, here logistic regression are achieved using the QN algorithm, second steps deals with choosing the most suitable unlabeled samples.…”

Section: Hyperspectral Image Crop Identification and Classificationmentioning

confidence: 99%

A Efficient Solution for Classification of Crops using Hyper Spectral Satellite Images

Babu*,

Padma

2019

IJITEE

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Crop identification (CI) utilizing hyperspectral pictures/images (HSI) collected from satellite is one of the effective research area considering various agriculture related applications. Wide range of research activity is carried out and modelled in the area of crop recognition (CR) for building efficient model. Correlation filter (CF) is considered to be one of an effective method and are been applied by existing methodologies for identifying similar signal features. Nonetheless, very limited is work is carried out using CF for crop classification using hyperspectral data. Further, effective method is required that bring good tradeoffs between memory and computational overhead. The crop classification model can be improved by combining machine learning (ML) technique with CF. HSI is composed of hundreds of channels with large data dimension that gives entire information of imaging. Thus, using classification model is very useful for real-time application uses. However, the accuracy of classification task is affected as HSI is composed of high number of redundant and correlated feature sets. Along with, induce computational overhead with less benefits using redundant features. Thus, effective band selection, texture analysis, and classification method is required for accurately classifying multiple crops. This paper analyses various existing techniques for identification and classification of crops using satellite imagery detection method. Then, identify the research issues, challenges, and problems of existing model for building efficient techniques for identification and classification of crops using satellite image. Experiment are conducted on standard hyperspectral data. The result attained shows proposed model attain superior classification accuracy when compared with existing hyperspectral image classification model.

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A novel active learning approach for the classification of hyperspectral imagery using quasi-Newton multinomial logistic regression

Cited by 15 publications

References 48 publications

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Multi-Spectral Image Classification Based on an Object-Based Active Learning Approach

A Efficient Solution for Classification of Crops using Hyper Spectral Satellite Images

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