Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data

Cheng, Qi; Varshney, Pramod K.; Arora, Manoj K.

doi:10.1109/lgrs.2006.877949

Cited by 116 publications

(55 citation statements)

References 12 publications

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“…9, No. 8, 2018 57 | P a g e www.ijacsa.thesai.org Logistic Regression semi-supervised [61] band selection [62] Gaussian Models transfer learning [63] [64]…”

Section: Discussionmentioning

confidence: 99%

The Role of Hyperspectral Imaging: A Literature Review

Mateen¹,

Wen²,

Nasrullah³

et al. 2018

ijacsa

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Abstract-Optical analysis techniques are used recently to detect and identify the objects from a large scale of images. Hyperspectral imaging technique is also one of them. Vision of human eye is based on three basic color (red, green and blue) bands, but spectral imaging divides the vision into many more bands. Hyperspectral remote sensors achieve imagery data in the form of hundreds of adjoining spectral bands. In this paper, our purpose is to illustrate the fundamental concept, hyperspectral remote sensing, remotely sensed information, methods for hyperspectral imaging and applications based on hyperspectral imaging. Moreover, in the forensic context, the novel methods involving deep neural networks are elaborated in this paper. The proposed idea can be useful for further research in the field of hyperspectral imaging using deep learning.

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“…9, No. 8, 2018 57 | P a g e www.ijacsa.thesai.org Logistic Regression semi-supervised [61] band selection [62] Gaussian Models transfer learning [63] [64]…”

Section: Discussionmentioning

confidence: 99%

The Role of Hyperspectral Imaging: A Literature Review

Mateen¹,

Wen²,

Nasrullah³

et al. 2018

ijacsa

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“…-Logistic regression method is also being applied in remote sensing data classification (Cheng et al, 2006). It fits multinomial log-linear models via neural networks.…”

Section: Classification Methodsmentioning

confidence: 99%

Benchmark of Machine Learning Methods for Classification of a Sentinel-2 Image

Pirotti

Sunar

Piragnolo

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4KEY WORDS: Machine learning, Sentinel-2, Remote sensing, Neural nets, Agriculture, Land cover, Classification ABSTRACT:Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km 2 , obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines -with default radial basis function kernel -methods follow closely with comparable performance.

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“…As a discriminative classifier, SVM has been proven to have promising performance for multispectral and hyperspectral remote sensing image classification [4,40,41]. The SVM classifier has better performance than multinomial logistic regression (MLR) [37] for HSI classification because by using the kernel function, the linearly inseparable spectral signatures are projected into a higher-dimension space to be separable. SVM needs fewer training samples compared to MLR under the structural risk minimization (SRM) principle [26].…”

Section: Higher Order Support Vector Random Fieldsmentioning

confidence: 99%

“…The class descriptions and the training and testing size of each class are shown in Table 1 (see Section 3.2). We compared the classification results of HSVRFs with those of MLR [37,48], SVM [2], CRFs, and SVRFMC [26]. In the second group of experiments, we kept the same training testing split of reference data as Zhong et al [27] did, and directly drew the classification results of MLR and CRF-H on Indian Pines from his work for comparison.…”

Section: Experimental Settingmentioning

confidence: 99%

Higher Order Support Vector Random Fields for Hyperspectral Image Classification

Yang

Jiang

Hao

et al. 2018

IJGI

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This paper addresses the problem of contextual hyperspectral image (HSI) classification. A novel conditional random fields (CRFs) model, known as higher order support vector random fields (HSVRFs), is proposed for HSI classification. By incorporating higher order potentials into a support vector random fields with a Mahalanobis distance boundary constraint (SVRFMC) model, the HSVRFs model not only takes advantage of the support vector machine (SVM) classifier and the Mahalanobis distance boundary constraint, but can also capture higher level contextual information to depict complicated details in HSI. The higher order potentials are defined on image segments, which are created by a fast unsupervised over-segmentation algorithm. The higher order potentials consider the spectral vectors of each of the segment's constituting pixels coherently, and weight these pixels with the output probability of the support vector machine (SVM) classifier in our framework. Therefore, the higher order potentials can model higher-level contextual information, which is useful for the description of challenging complex structures and boundaries in HSI. Experimental results on two publicly available HSI datasets show that the HSVRFs model outperforms traditional and state-of-the art methods in HSI classification, especially for datasets containing complicated details.

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Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data

Cited by 116 publications

References 12 publications

The Role of Hyperspectral Imaging: A Literature Review

The Role of Hyperspectral Imaging: A Literature Review

Benchmark of Machine Learning Methods for Classification of a Sentinel-2 Image

Higher Order Support Vector Random Fields for Hyperspectral Image Classification

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