Analysis of uncertainty ratio in classified imagery using independent indicator entropy

Sharma, Ranjana; Garg, Pooja; Dwivedi, R.

doi:10.1016/j.ejrs.2018.04.007

Cited by 3 publications

(1 citation statement)

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“…The so-called full pixel techniques, adopting the assumption that each pixel is associated with a pure land cover type, have been evaluated as no appropriate for the processing of mixed pixels. A widely alternative way is the use of soft classification approaches [9]. These methods do not appoint a pixel to exactly one class, although they generate a set of fractions that reflects, for each pixel, the membership amplitude for a given class.…”

Section: Related Workmentioning

confidence: 99%

Design of Multi-View Graph Embedding for Features Selection and Remotely Sensing Signal Classification

Alotaibi¹,

Alotaibi²

2020

IJACSA

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Now-a-days, signal processing remains an intensive challenging area of research. In fact, various strategies have been suggested to address semi-supervised, feature selection and unlabeled samples challenges. The most frequent achievement was dedicated to exploit a single kind of feature/view from the original data. Recently, advanced techniques aimed to explore signals from different views and to, properly, integrate divergent kinds of interdependent features. In this paper, we propose a novel design of a multi-View Graph Embedding for features selection allowing a convenient integration of complementary weighted features. The proposed framework combines the singular properties of each feature space to accomplish a physically meaningful cooperative low-dimensional selection of input data. This allows us not only to perform a semi-supervised classification, but also to propagates narrow class information to unlabeled sample when only partial labeling knowledge is available. This paper makes the following contributions: (i) a feature selection schema for data refinement; and (ii) the adaptation of a multi-view graph-based approach by a better tackling of semi-supervised and dimensionality issues. Our experimental results, conducted by using a mixture of complementary features and aerial images datasets, demonstrate the effectiveness of the proposed framework without significantly increasing computational complexity.

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Section: Related Workmentioning

confidence: 99%