Statistical pattern recognition in remote sensing

Chen, Chi Hau; Ho, Pei-Gee Peter

doi:10.1016/j.patcog.2008.04.013

Cited by 92 publications

(45 citation statements)

References 35 publications

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“…Feedforward backpropagation neural networks are well established as effective algorithms for use in image classification (e.g., [50][51][52]). Neural networks are especially useful for the mapping of geological materials, since individual geological classes are commonly characterized by substantial variation in reflectance properties as a result of spatial inhomogeneities in mineralogy, degree of chemical alteration, and surface exposure [44,53].…”

Section: Neural Network Classificationmentioning

confidence: 99%

Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada

Leverington

Moon

2012

Remote Sensing

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Abstract:In order to better constrain the utility of multispectral datasets in the characterization of surface materials, Landsat Thematic Mapper (TM) data were evaluated in the discrimination of geological classes in the Cape Smith Belt of Quebec, a greenstone belt that hosts Early Proterozoic units including those of the Purtuniq ophiolite. Ground-based measurements collected for the study area highlight the importance of chemical alteration in controlling the reflectance properties of key geological classes. The spatial distribution of exposed lithologies in the study area was determined through (1) image classification using a feedforward backpropagation neural network classifier; and (2) generation of fraction images for spectral end members using a linear unmixing algorithm and ground reflectance data. Despite some shortcomings, the database of surface cover generated by the neural network classifier is a useful representation of the spatial distribution of exposed geological materials in the study area, with an overall agreement with ground truth of 87.7%. In contrast, the fraction images generated through unmixing are poor representations of ground truth for several key lithological classes. These results underscore both the considerable utility and marked limitations of Landsat TM data in the mapping of igneous and metamorphic lithologies.

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Section: Neural Network Classificationmentioning

confidence: 99%

Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada

Leverington

Moon

2012

Remote Sensing

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“…The SVM approach uses the principle of structural risk minimization, not the principle of empirical risk minimization [67]. Therefore, the performance of SVM has been examined by many studies [68][69][70][71].…”

Section: Random Forest and Support Vector Machine For Classificationmentioning

confidence: 99%

Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data

Hasituya¹,

Chen

2017

Remote Sensing

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Using plastic mulching for farmland is booming around the world. Despite its benefit of protecting crops from unfavorable conditions and increasing crop yield, the massive use of the plastic-mulching technique causes many environmental problems. Therefore, timely and effective mapping of plastic-mulched farmland (PMF) is of great interest to policy-makers to leverage the trade-off between economic profit and adverse environmental impacts. However, it is still challenging to implement remote-sensing-based PMF mapping due to its changing spectral characteristics with the growing seasons of crops and geographic regions. In this study, we examined the potential of multi-temporal Landsat-8 imagery for mapping PMF. To this end, we gathered the information of spectra, textures, indices, and thermal features into random forest (RF) and support vector machine (SVM) algorithms in order to select the common characteristics for distinguishing PMF from other land cover types. The experiment was conducted in Jizhou, Hebei Province. The results demonstrated that the spectral features and indices features of NDVI (normalized difference vegetation index), GI (greenness index), and textural features of mean are more important than the other features for mapping PMF in Jizhou. With that, the optimal period for mapping PMF is in April, followed by May. A combination of these two times (April and May) is better than later in the season. The highest overall, producer's, and user's accuracies achieved were 97.01%, 92.48%, and 96.40% in Jizhou, respectively.

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“…The main advantage of fuzzy classification based method includes its applicability for very complex processes. The flow of classification using fuzzy logic is described below [31].…”

Section: Fig1 Fuzzy Logic Conceptmentioning

confidence: 99%

A Fruit Detecting and Grading System Based on Image Processing-Review

Banot¹,

P.M.²

2016

International Journal of Innovative Research in Electrical, Ele

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This work presents a literature review study for fruit detecting and grading system based on image processing techniques. Different types of algorithms are available to extract feature of fruit characters by capturing the fruit image. With the help of this feature fruit is detected and graded according to quality of fruit this can be done using different types of classifier. The grading system has the advantage of high accuracy, high speed and low cost. It will have a good scene of application in fruit quality detecting and grading areas. This work aimed to study different types of algorithms used for quality grading to developed an algorithm for detecting and sorting of fruit from the acquire image. The feature such as morphological feature is used and colour can be extracted which is further more used to identify class of the fruit using neural network.

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Statistical pattern recognition in remote sensing

Cited by 92 publications

References 35 publications

Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada

Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada

Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data

A Fruit Detecting and Grading System Based on Image Processing-Review

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