Cascaded Random Forest for Hyperspectral Image Classification

Zhang, Youqiang; Cao, Guo; Li, Xuesong; Wang, Bisheng

doi:10.1109/jstars.2018.2809781

Cited by 87 publications

(45 citation statements)

References 54 publications

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“…In the classification, the forest chooses the class having the most votes (over all the trees in the forest). In RF classification a split is determined by searching across a random subset of variables at each node [36,37].…”

Section: Random Forest Classifiermentioning

confidence: 99%

Hyperspectral Image Classification

Gogineni¹,

Chaturvedi²

2020

Processing and Analysis of Hyperspectral Data

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Hyperspectral image (HSI) classification is a phenomenal mechanism to analyze diversified land cover in remotely sensed hyperspectral images. In the field of remote sensing, HSI classification has been an established research topic, and herein, the inherent primary challenges are (i) curse of dimensionality and (ii) insufficient samples pool during training. Given a set of observations with known class labels, the basic goal of hyperspectral image classification is to assign a class label to each pixel. This chapter discusses the recent progress in the classification of HS images in the aspects of Kernel-based methods, supervised and unsupervised classifiers, classification based on sparse representation, and spectral-spatial classification. Further, the classification methods based on machine learning and the future directions are discussed.

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Section: Random Forest Classifiermentioning

confidence: 99%

Hyperspectral Image Classification

Gogineni¹,

Chaturvedi²

2020

Processing and Analysis of Hyperspectral Data

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“…Random forest has many advantages, including high speed, strong parallelism, noise robustness, and an inherently multi-class nature. Due to these features, it is widely used in remote sensing image analysis [9][10][11][17][18][19][20][21][22][23][24]. However, RF suffers from the same problem as other popular classification methods: it requires many labeled samples to leverage its full potential.…”

Section: Semi-supervised Random Forestmentioning

confidence: 99%

“…Many of these applications are based on hyperspectral image (HSI) classification at the pixel level. In the past few years, various supervised classification methods, e.g., support vector machines (SVMs) [5,6], neural networks [7,8], and random forests (RFs) [9][10][11] have been successfully used for HSI classification. However, supervised methods often require many informative samples with labels to train high-performing classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results have shown that our proposed method outperforms other state-of-the-art methods.Random forest, proposed by Breiman [16], is one of the most important machine learning methods. Compared with other machine learning methods, RF has the following advantages [9][10][11][17][18][19][20][21][22][23][24]. First, RF is suitable for high-dimensional data, which can alleviate the influence of curse of dimensionality [20].…”

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confidence: 99%

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Active Semi-Supervised Random Forest for Hyperspectral Image Classification

Zhang

Cao

et al. 2019

Remote Sensing

Self Cite

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Random forest (RF) has obtained great success in hyperspectral image (HSI) classification. However, RF cannot leverage its full potential in the case of limited labeled samples. To address this issue, we propose a unified framework that embeds active learning (AL) and semi-supervised learning (SSL) into RF (ASSRF). Our aim is to utilize AL and SSL simultaneously to improve the performance of RF. The objective of the proposed method is to use a small number of manually labeled samples to train classifiers with relative high classification accuracy. To achieve this goal, a new query function is designed to query the most informative samples for manual labeling, and a new pseudolabeling strategy is introduced to select some samples for pseudolabeling. Compared with other AL-and SSL-based methods, the proposed method has several advantages. First, ASSRF utilizes the spatial information to construct a query function for AL, which can select more informative samples. Second, in addition to providing more labeled samples for SSL, the proposed pseudolabeling method avoids bias caused by AL-labeled samples. Finally, the proposed model retains the advantages of RF. To demonstrate the effectiveness of ASSRF, we conducted experiments on three real hyperspectral data sets. The experimental results have shown that our proposed method outperforms other state-of-the-art methods.Random forest, proposed by Breiman [16], is one of the most important machine learning methods. Compared with other machine learning methods, RF has the following advantages [9][10][11][17][18][19][20][21][22][23][24]. First, RF is suitable for high-dimensional data, which can alleviate the influence of curse of dimensionality [20]. Second, RF is very fast because it is implemented in parallel [9]. Third, RF is effective to handle data with imbalanced class labels or missing values [21]. Fourth, RF is not easy to fall into overfitting compared with the Boosting method [18]. Moreover, RF measures the importance of variables automatically [25]. Finally, RF can obtain a higher classification accuracy compared to other well-known classifiers such as SVM [5,6] and maximum likelihood (ML) [17,23], with fewer parameters.Active learning is a kind of iterative method that queries the most informative samples for manual labeling at each iteration [14]. Initially, the data set consists of two parts: a small number of labeled samples and a large number of unlabeled samples. At each iteration, a query function is adopted to investigate the unlabeled samples and select the most informative samples for manual labeling. After multiple iterations, the performance of the classifier improves along with the increase in the number of manually labeled samples. The query function plays an important role in determining the samples selected for manual labeling, which directly affects the classification performance [15].Semi-supervised learning, another way to tackle the issue of limited samples, trains a classifier with the labeled and unlabeled samples together, which does not r...

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“…The results suggest that the method may be of assistance to image processing applications which rely on a transformation for data reduction as a first step of further processing. For examples of relevant applications we refer to [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Sparse representation of 3D images for piecewise dimensionality reduction with high quality reconstruction

Rebollo-Neira

Whitehouse

2019

Array

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Sparse representation of 3D images is considered within the context of data reduction. The goal is to produce high quality approximations of 3D images using fewer elementary components than the number of intensity points in the 3D array. This is achieved by means of a highly redundant dictionary and a dedicated pursuit strategy especially designed for low memory requirements. The benefit of the proposed framework is illustrated in the first instance by demonstrating the gain in dimensionality reduction obtained when approximating true color images as very thin 3D arrays, instead of performing an independent channel by channel approximation. The full power of the approach is further exemplified by producing high quality approximations of hyper-spectral images with a reduction of up to 371 times the number of data points in the representation.

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Cascaded Random Forest for Hyperspectral Image Classification

Cited by 87 publications

References 54 publications

Hyperspectral Image Classification

Hyperspectral Image Classification

Active Semi-Supervised Random Forest for Hyperspectral Image Classification

Sparse representation of 3D images for piecewise dimensionality reduction with high quality reconstruction

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