Application of Multiple-Instance Learning for Hyperspectral Image Analysis

Bolton, Jeremy; Gader, Paul D.

doi:10.1109/lgrs.2011.2135330

Cited by 25 publications

(15 citation statements)

References 12 publications

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“…In general MIL methods are shown to perform better than single instance learning schemes, and therefore, have seen application in remote sensing image classification as well. For example, in [6], authors have developed an MIL based binary classification scheme for identifying targets (landmines) in Hyperspectral (HS) imagery. The high computational cost of Citation-KNN has lead to the development of an efficient Gaussian Multiple Instance (GMIL) [23] learning algorithm.…”

Section: Previous Workmentioning

confidence: 99%

An efficient computational framework for labeling large scale spatiotemporal remote sensing datasets

Sethi

Yan

Rangarajan

et al. 2014

2014 Seventh International Conference on Contemporary Computing (IC3)

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We present a novel framework for semisupervised labeling of regions in remote sensing image datasets. Our approach works by decomposing the image into irregular patches or superpixels and derives novel features based on intensity histograms, geometry, corner density, and scale of tessellation. Our classification pipeline uses either k-nearest neighbors or SVM to obtain a preliminary classification which is then refined using Laplacian propagation algorithm. Our approach is easily parallelizable and fast despite the high volume of data involved. Results are presented which showcase the accuracy as well as different stages of our pipeline.

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Section: Previous Workmentioning

confidence: 99%

An efficient computational framework for labeling large scale spatiotemporal remote sensing datasets

Sethi

Yan

Rangarajan

et al. 2014

2014 Seventh International Conference on Contemporary Computing (IC3)

View full text Add to dashboard Cite

show abstract

“…For example, in [14] MIL approach is explored for sub-surface landmine detection using hyperspectral (HS) imagery. In [2], authors have developed MIL based binary classification scheme for identifying targets (landmines) in HS imagery. While each of these algorithms have advantages and disadvantages over per-pixel based classification schemes, in general they are shown to perform (accuracy) better than single instance learning schemes.…”

Section: Related Work and Our Contributionsmentioning

confidence: 99%

Complex settlement pattern extraction with multi-instance learning

Vatsavai

Bhaduri

Graesser

2013

Joint Urban Remote Sensing Event 2013

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Per-pixel (or single instance) based classification schemes which have proven to be very useful in thematic classification have shown to be inadequate when it comes to analyzing very high resolution remote sensing imagery. The main problem being that the pixel size (less than a meter) is too small as compared to the typical object size (100s of meters) and contains too little contextual information to accurately distinguish complex settlement types. One way to alleviate this problem is to consider a bigger window or patch/segment consisting a group of adjacent pixels which offers better spatial context than a single pixel. Unfortunately, this makes per-pixel based classification schemes ineffective. In this work, we look at a new class of machine learning approaches, called multi-instance learning, where instead of assigning class labels to individual instances (pixels), a label is assigned to the bag (all pixels in a window or segment). We applied this multi-instance learning approach for identifying two important urban patterns, namely formal and informal settlements. Experimental evaluation shows the better performance of multi-instance learning over several well-known single-instance classification schemes.

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“…10 This approach has shown to improve classification results as compared to its non-MI counterpart, the standard RVM. 11 However, the increase in performance comes at the cost of increased computational complexity during the learning stage. The reason for this increase in complexity is the use of the noisy-OR.…”

Section: Multiple Instance Learningmentioning

confidence: 99%

Embedding the multiple instance problem: applications to landmine detection with ground penetrating radar

2013

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Multiple Instance Learning is a recently researched learning paradigm in machine intelligence which operates under conditions of uncertainty with the cost of increased computational burden. This increase in computational burden can be avoided by embedding these so-called multiple instances using a kernel function or other embedding function. In the following, a family of fast multiple instance relevance vector machines are used to learn and classify landmine signatures in ground penetrating radar data. Results indicate a significant reduction in computational complexity without a loss in classification accuracy in operating conditions.

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Application of Multiple-Instance Learning for Hyperspectral Image Analysis

Cited by 25 publications

References 12 publications

An efficient computational framework for labeling large scale spatiotemporal remote sensing datasets

An efficient computational framework for labeling large scale spatiotemporal remote sensing datasets

Complex settlement pattern extraction with multi-instance learning

Embedding the multiple instance problem: applications to landmine detection with ground penetrating radar

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