An artificial neural network (ANN) based software package for classification of remotely sensed data

Mohanty, K. K.; Majumdar, T. J.

doi:10.1016/0098-3004(95)00059-3

Cited by 22 publications

(6 citation statements)

References 6 publications

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“…The method is used to minimise the error function simultaneously and proceed with feedforward back-error propagation algorithm operation using multi-layer perception. From the input layer to receive independent variables while the model is running, the system will then proceed with the tasking of nonlinear variable relation, and create network weighting and linking relationship model, especially, the tasking adopts the sigmoid activation function (Mohanty and Majumdar 1996;Zhang et al 2019). The sigmoid function, also known as the logistic function, is the one that determines the output of the neuron in a neural network based on the input.…”

Section: Back-propagation Network (Bpn)mentioning

confidence: 99%

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Wang

Lin

Chu

et al. 2021

Enterprise Information Systems

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This study aims to incorporate the application of RS technology with management strategies and proposes the Remote Sensing Knowledge-Based Decision Support System (RS-KBDSS) framework. It is not only focusing on considering RS technology for discussing the accuracy of estimation but also conducting strategic thinking that is needed for keeping the inventory and monitoring task running by obtaining knowledge and decision support. This study offers more flexible and accurate estimation models for different resolution images and different kinds of models. The results obtained by the study is satisfied with the practical application.

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Section: Back-propagation Network (Bpn)mentioning

confidence: 99%

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Wang

Lin

Chu

et al. 2021

Enterprise Information Systems

View full text Add to dashboard Cite

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“…In most of the research work of multiclass classification good images are taken for classification ([2], [3], [4], [14)]. Since noise affects the performance of features extracted for classification.…”

Section: Noise Type Detectionmentioning

confidence: 99%

“…A comparison to conventional supervised classification by using minimal training set in Artificial Neural Network is given in [13]. Remotely sensed data by using Artificial Neural Network based have been classified in [14] on software package.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Performance of Multi Class Classification of Anonymous Noisy Images

Singh¹,

Shukla²,

Biradar³

et al. 2014

IJIGSP

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An important constituents for image classification is the identification of significant characterstics about the specific class to distinguish intra class variations. Since performance of the classifiers is affected in the presence of noise, so selection of discriminative features is an important phase in classification. This superfluous information i.e. noise, e.g. additive noise may occur in images due to image sensors i.e. of the constant noise level in dark areas of the image or salt & pepper noise may be caused by analog to digitals conversion and bit error transmission etc.. Detection of noise is also very essential in the images for choosing appropriate filter. This paper presents an experimental assessment of neural classifier in terms of classification accuracy under three different constraints of images without noise, in presence of unknown noise and after elimination of noise.

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“…A comparison to conventional supervised classification by using minimal training set in Artificial Neural Network is given in [12]. Remotely sensed data by using Artificial Neural Network based have been classified in [13] on software package. In [14] different types of noise are classified using feed forward neural network.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Texture Image Classification Using Wavelet Feature

Choudhary¹,

Singh²,

Tiwari³

et al. 2013

IJIGSP

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This paper compares the performance of various classifiers for multi class image classification. Where the features are extracted by the proposed algorithm in using Haar wavelet coefficient. The wavelet features are extracted from original texture images and corresponding complementary images. As it is really very difficult to decide which classifier would show better performance for multi class image classification. Hence, this work is an analytical study of performance of various classifiers for the single multiclass classification problem. In this work fifteen textures are taken for classification using Feed Forward Neural Network, Naï ve Bays Classifier, K-nearest neighbor Classifier and Cascaded Neural Network.

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An artificial neural network (ANN) based software package for classification of remotely sensed data

Cited by 22 publications

References 6 publications

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Enhanced Performance of Multi Class Classification of Anonymous Noisy Images

Performance Analysis of Texture Image Classification Using Wavelet Feature

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