Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image

Senthilnath, J.; Karnwal, Nitin; Teja, D. Sai

doi:10.5815/ijigsp.2014.09.02

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…Also known as Kohonen maps, the self-organizing maps represent a special class of single layer neural networks trained using unsupervised learning, introduced by Tuevo Kohonen in 1982 in [4] and further developed in his subsequent research (see, for instance, [6] [7]). …”

Section: A Self-organizing Mapsmentioning

confidence: 99%

Evaluating Image Recognition Efficiency using a Self-Organizing Map

Ligeiro¹,

Utkin²

2016

IJIGSP

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Abstract-Recognition and classification of images is an extremely topical interdisciplinary area that covers image processing, computer graphics, artificial intelligence, computer vision, and pattern recognition, resulting in many applications based on contemporary mobile devices. Developing reliable recognition schemes is a difficult task to accomplish. It depends on many factors, such as illumination, acquisition quality and the database images, in particular, their diversity. In this paper we study how the data diversity affects decision making in image recognition, presenting a database driven classificationerror predictor. The predictor is based on a hybrid approach that combines a self-organizing map together with a probabilistic logical assertion method. By means of a clustering approach, the model provides fast and efficient assessment of the image database heterogeneity and, as expected, indicates that such heterogeneity is of paramount importance for robust recognition. The practicality of the model is demonstrated using a set of image samples collected from a standard traffic sign database publicly available by the UK Department for Transport.

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Section: A Self-organizing Mapsmentioning

confidence: 99%

Evaluating Image Recognition Efficiency using a Self-Organizing Map

Ligeiro¹,

Utkin²

2016

IJIGSP

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“…The initial clusters are obtained independently for the given image by different clustering techniques assuming that the number of clusters is known. A set of reliable samples for each cluster is identified to be used for the initialisation of an iterative expectation-maximisation-based retraining (Senthilnath et al, 2014). The EM algorithm approximates cluster parameters assuming that clusters are Gaussian distributed.…”

Section: Introductionmentioning

confidence: 99%

Multi spectral image classification using cluster ensemble technique

Radhika

Varadarajan²,

Muralimohanbabu³

2018

IJISTA

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Satellite image classification is an imperative system utilised as a part of remote sensing. Primary data of extraordinary significance to different difficulties can be acquired straightforwardly from Land-cover observation. Different information partitions inferred by various clustering methods can be gathered into a new solution by cluster ensembles. Supervised iterative expectation-maximisation (EM) method can be initialised by cluster ensemble based strategy which will be examined in the paper. The performance of clustering of the proposed method is compared with individual clustering of the ensemble for medium resolution and a very high spatial resolution images. The accuracy measurements have done with different test points. The state of art techniques are giving less accuracy and are not well defined. This paper will explore the possibility of all accuracy parameters with supervised classification results. The accuracy parameters are well tested and compared in this paper with various start of art techniques.

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“…In supervised techniques, the training areas are used which are homogeneous representative samples of the different surface types of interest. All the spectral bands of the pixels comprising these areas have numerical information [11]. The algorithm assign some pixels to information classes based on fieldwork, map analysis, and personal experience.…”

Section: Introductionmentioning

confidence: 99%

Supervised Classification Approaches to Analyze Hyperspectral Dataset

El_Rahman¹,

Aliady²,

Alrashed³

2015

IJIGSP

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In this paper, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) classification approaches were used to classify hyperspectral image of Georgia, USA, using Environment of Visualizing Images (ENVI). It is a software application used to process and analyze geospatial imagery. Spatial, spectral subset and atmospheric correction have been performed for SAM and SID algorithms. Results showed that classification accuracy using the SAM approach was 72.67%, and SID classification accuracy was 73.12%. Whereas, the accuracy of SID approach is better than SAM approach. Consequently, the two approaches (SID and SAM) have proven to be accurately converged in classification of hyperspectral image of Georgia, USA.

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Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image

Cited by 7 publications

References 31 publications

Evaluating Image Recognition Efficiency using a Self-Organizing Map

Evaluating Image Recognition Efficiency using a Self-Organizing Map

Multi spectral image classification using cluster ensemble technique

Supervised Classification Approaches to Analyze Hyperspectral Dataset

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