Land cover classification of SSC image: unsupervised and supervised classification using ERDAS imagine

Long, Wei; Srihann, S.

doi:10.1109/igarss.2004.1369859

Cited by 17 publications

(10 citation statements)

References 2 publications

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“…Wilbert Long [2] has defined a work to analyse the land area based on the identification of soil identification. The images are captured from the satellite and the region classification and segmentation is done.…”

Section: Existing Workmentioning

confidence: 99%

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A study on agricultural image processing along with classification model

Chahal

2015

2015 IEEE International Advance Computing Conference (IACC)

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Agricultural image processing is one of most innovative and important image processing areas recognized in last few years. Because of the vast range of associated sub domain it is having the current attention of the researchers. In this paper, the exploration of different domains associated with agricultural image processing is defined. The paper has also explored the recognition model with broader view. The paper has presented a generalized framework for plant disease classification and recognition. The paper has also defined a study on some of the effective classification approaches including SVM, Neural Network, KMeans and PCA.

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Section: Existing Workmentioning

confidence: 99%

“…Long [2] A Land area based image analysis work is been defined to identify the soil regions. The classification and segmentation is defined under rule structural approach.…”

Section: Wilbertmentioning

confidence: 99%

A study on agricultural image processing along with classification model

Chahal

2015

2015 IEEE International Advance Computing Conference (IACC)

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“…This hybrid algorithm (BCSCA) is also validated with the same multi-spectral satellite image dataset of Alwar (Rajasthan, India). Classification of satellite images for extracting information about various land cover/land use is a very complex process and needs in depth analysis, as the results attained forms the basis for many environmental and socio-economic applications [2]. The results obtained with the real time dataset are again quite satisfactory and can be improved further by somewhat more adjustment of the parameters.…”

Section: Introductionmentioning

confidence: 97%

“…Analysis of waterbodies and demarcation of its features properly is the foremost step for any planning, particularly for regions, where the land-cover is dominated by waterbodies. Over the past decade, a significant amount of research has been carried out to extract the water body information from various multi-resolution satellite images [2,6,7]. We have used the multi-spectral satellite image dataset of Alwar (Rajasthan, India) and Delhi (India) for extraction of water.…”

Section: Introductionmentioning

confidence: 99%

Novel approaches for classification based on Cuckoo Search Strategy

Goel

Sharma

Bedi

2013

HIS

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This paper introduces a novel bio inspired clustering algorithm called Cuckoo Search Clustering Algorithm (CSCA). This algorithm is based on the recently proposed Cuckoo Search Optimization technique which mimics the breeding strategy of the parasitic bird-cuckoo. The algorithm is further extended to a classification method, Biogeography Based Cuckoo Search Classification Algorithm (BCSCA), which is a hybrid approach of the two nature inspired metaheuristic techniques. The proposed algorithms are validated with real time remote sensing satellite image datasets. The CSCA was first tested with benchmark dataset, which yields good results. Inspired by the results, it was applied on two real time remote sensing satellite image datasets for extraction of the water body, which itself is a quite complex problem. A new method for the generation of new cuckoos has been proposed, which is used in the algorithms. The resulting algorithm is conceptually simpler, takes less parameter than other nature inspired algorithms, and, after some parameter tuning, yields very good results. The extended algorithm BCSCA is also tested on the same satellite image for identifying different land covers by classifying the image in various classes. The algorithm was successful in classifying other land cover regions like rocky, barren, urban and vegetation. We strongly feel that results can be further improved by finer tuning of the parameters. Both the algorithms use Davies-Bouldin index (DBI) as fitness function. Further exploration of suggested algorithms CSCA and BCSCA may prove them to be strong entrants in the pool of nature inspired techniques.

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“…In remote sensing the problem of feature extraction has been solved by using the traditional classical approaches of artificial intelligence like Parallel-o-piped Classification [11,22], Minimum Distance to Mean Classification [11,22], Maximum Likelihood Classification [11,22] etc. A major disadvantage of the above traditional AI techniques of natural terrain feature extraction is that these techniques show limited accuracy in information retrieval and high-resolution satellite image is needed.…”

Section: Nature Inspired CI Techniques For Geo-spatial Feature Ementioning

confidence: 99%

Taxonomy of nature inspired computational intelligence: A remote sensing perspective

Goel

Gupta

Panchal

et al. 2012

2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)

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---The concepts in geospatial sciences are generally vague, ambiguous and imprecise. Also, a combination of spectral, spatial and radiometric resolution of space-borne sensors presents a selective and incomplete look of the geospatial feature/object under its view from the space. Recently, the nature inspired computational intelligence (CI) techniques have emerged as an efficient mechanism to handle diverse uncertainty characteristics. This paper proposes that the human-mind model based computational intelligence techniques, the artificial immune system based computational intelligence techniques; the swarm intelligence based computational intelligence techniques and the emerging geo-sciences based intelligent techniques can be considered as the four pillars of nature inspired CI techniques and hence redefines and extends the taxonomy of nature inspired CI. Researchers have shown keen interest on the applications of natural computing in divergent domains. Scanty references are available on the applications of nature inspired computing in the area of remote sensing. We hence also propose the taxonomy of the most recent nature inspired CI techniques that have been adapted till date for geo-spatial feature extraction and analyze their performances. We also construct a technology timeline of these recent nature inspired CI techniques.Keywords---Natural computation; computational intelligence; geo-spatial feature extraction.

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Land cover classification of SSC image: unsupervised and supervised classification using ERDAS imagine

Cited by 17 publications

References 2 publications

A study on agricultural image processing along with classification model

A study on agricultural image processing along with classification model

Novel approaches for classification based on Cuckoo Search Strategy

Taxonomy of nature inspired computational intelligence: A remote sensing perspective

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