A Fast Cluster-Assumption Based Active-Learning Technique for Classification of Remote Sensing Images

Patra, Swarnajyoti; Bruzzone, Lorenzo

doi:10.1109/tgrs.2010.2083673

Cited by 76 publications

(29 citation statements)

References 21 publications

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“…According to different criteria, SVM active learning can be divided into different types, including binary-class SVM active learning, multiclass SVM active learning [5][6], single-label SVM active learning [5], multi-label SVM active learning [7], sequential SVM active learning and batch-mode SVM active learning [8][9][10] etc.…”

Section: Related Workmentioning

confidence: 99%

“…Nowadays, batch-mode AL is the mainstream, and sequential AL is seldom used since it is time-consuming. The work in [8][9][10] belongs to the batch-mode SVM active learning. In [9], Tuia et al proposed to select samples according to the closeness degree between samples and the current separating hyperplane, besides, in order to reduce redundancy, the authors added the constraint that no selected sample should share the closest support vector.…”

Section: Related Workmentioning

confidence: 99%

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A SVM Active Learning Algorithm Based on Class Boundary Characteristics, and Its Application in Audio Classification

Leng¹,

Zhou

Sun

et al. 2017

IJMUE

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A SVM Active Learning Algorithm Based on Class Boundary Characteristics, and Its Application in Audio Classification

Leng¹,

Zhou

Sun

et al. 2017

IJMUE

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show abstract

“…• Clustering algorithms: (Patra and Bruzzone, 2011) are using a cluster-assumption based approach and use a simple histogram-thresholding algorithm to evaluate the usefulness of the unlabeled samples.…”

Section: Structure Analysismentioning

confidence: 99%

Concept for a Compound Analysis in Active Learning for Remote Sensing

Wuttke

Middelmann

Stilla

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Active learning reduces training costs for supervised classification by acquiring ground truth data only for the most useful samples. We present a new concept for the analysis of active learning techniques. Our framework is split into an outer and an inner view to facilitate the assignment of different influences. The main contribution of this paper is a concept of a new compound analysis in the active learning loop. It comprises three sub-analyses: structural, oracle, prediction. They are combined to form a hypothesis of the usefulness for each unlabeled training sample. Though the analysis is in an early stage, different extensions are highlighted. Further we show how variations inside the framework lead to many techniques from the active learning literature. In this work we focus on remote sensing, but the proposed method can be applied to other fields as well.

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“…The first is governed by the Smoothness Assumption (Schindler, 2012) which states that pixels have a higher probability of belonging to the same class if they are spatially closer. The second information source is governed by the Cluster Assumption (Patra and Bruzzone, 2011) which states that pixels have a higher probability of belonging to the same class if they are spectrally closer. These information sources are called redundancies by (Hasanzadeh and Kasaei, 2010).…”

Section: Introductionmentioning

confidence: 99%

Improving Active Queries With a Local Segmentation Step and Application to Land Cover Classification

Wuttke

Middelmann

Stilla³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Active queries is an active learning method used for classification of remote sensing images. It consists of three steps: hierarchical clustering, dendrogram division, and active label selection. The goal of active learning is to reduce the needed amount of labeled data while preserving classification accuracy. We propose to apply local segmentation as a new step preceding the hierarchical clustering. We are using the SLIC (simple linear iterative clustering) algorithm for dedicated image segmentation. This incorporates spatial knowledge which leads to an increased learning rate and reduces classification error. The proposed method is applied to six different areas of the Vaihingen dataset.

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A Fast Cluster-Assumption Based Active-Learning Technique for Classification of Remote Sensing Images

Cited by 76 publications

References 21 publications

A SVM Active Learning Algorithm Based on Class Boundary Characteristics, and Its Application in Audio Classification

A SVM Active Learning Algorithm Based on Class Boundary Characteristics, and Its Application in Audio Classification

Concept for a Compound Analysis in Active Learning for Remote Sensing

Improving Active Queries With a Local Segmentation Step and Application to Land Cover Classification

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