A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data

Gong, Peng; Marceau, Danielle J.; Howarth, Philip J.

doi:10.1016/0034-4257(92)90011-8

Cited by 272 publications

(153 citation statements)

References 13 publications

Supporting

Mentioning

152

Contrasting

Unclassified

Order By: Relevance

“…Out of the 236 985 image pixels, expert photointerpreters labeled two spatially separated sets of pixels with their correct land-cover class. Due to their a priori information on the territorial variability, they focused on seven land-cover classes: vegetated land (class 1), built-up area (2), pine wood (3), urban green (4), greenhouses (5), not-vegetated land (6), and water (7). We asked the photointerpreter to find pixels mainly occupied by only one class in order to have a hard-labeled data set.…”

Section: A Data Set I Descriptionmentioning

confidence: 99%

“…This study focuses on multiclass land-cover classification of multispectral and multisource remotely sensed images with different spatial resolutions: A high-resolution image registered by the IKONOS sensor (4 m/pixel) and a low-medium-resolution image registered by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor (15 m/pixel) have been used. To exploit the high-resolution images, we extracted some textural features using the gray-level co-occurrence matrix (GLCM) [6], [7] and merged them with spectral information taken from the medium-resolution images. We compared the aforementioned classification methods, trained on hard-labeled data, and studied their performances in terms of overall accuracy on hardlabeled data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Giacco

Thiel

Pugliese

et al. 2010

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Abstract-Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracyassessment techniques in image classification, but a common approach is not yet universally adopted. We look at the relationship between hard accuracy and the uncertainty on the produced answers, introducing two measures based on maximum probability and α quadratic entropy. Their impact differs depending on the type of classifier. In this paper, we deal with two different classification strategies, based on support vector machines (SVMs) and Kohonen's self-organizing maps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results.

show abstract

Section: A Data Set I Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Giacco

Thiel

Pugliese

et al. 2010

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The overall accuracy was calculated for summary measures (Gong et al, 1992), which can be used to compare individual class difference between distinct classifications (Coburn and Roberts, 2004).…”

Section: Accuracymentioning

confidence: 99%

Forest type mapping using object-based classification method in Kapilvastu district, Nepal

Chaudhary

Acharya

Khanal

2016

Banko

View full text Add to dashboard Cite

In the recent years, object-based image analysis (OBIA) approach has emerged with an attempt to overcome limitations inherited in conventional pixel-based approaches. OBIA was performed using Landsat 8 image to map the forest types in Kapilvastu district of Nepal. Systematic sampling design was adopted to establish sample points in the field, and 70% samples were used for classification and 30% samples for accuracy assessment. Landsat image was pre-processed, and the slope and aspect derived from the ASTER DEM were used as additional predictors for classification. Segmentation was done using eCognition v8.0 with the scale parameter of 20, ratios of 0.1 and 0.9 for shape and color, respectively. Classification and Regression Tree (CART) and nearest neighbor classifier (k-NN) methods were used for object-based classification. The major forest types observed in the district were KS (Acacia catechu/ Dalbergia sissoo), Sal (Shorea robusta) and Tropical Mixed Hardwood. The k-NN classification technique showed higher overall accuracy than the CART method. The classification approach used in this study can also be applied to classify forest types in other districts. Improvement in classification accuracy can be potentially obtained through inclusion of sufficient samples from all classes. K e y w or d s :Landsat, machine learning algorithm, object-based classification

show abstract

“…For instance, Franklin and Peddle [7] raised the classification accuracy of SPOT HRV imagery from 51.1% (spectral alone) to 86.7% by adding the GLCM features created from one of the SPOT HRV bands into the spectral space. Gong et al [8] used the GLCM textures extracted from one of the SPOT XS bands, as well as the multispectral information, to improve land-use classification. Puissant et al [9] confirmed that the use of GLCM textures created from the panchromatic band was able to significantly improve the per-pixel classification accuracy for high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation

2014

View full text Add to dashboard Cite

This study proposes a novel method for multichannel image gray level co-occurrence matrix (GLCM) texture representation. It is well known that the standard procedure for the automatic extraction of GLCM textures is based on a mono-spectral image. In real applications, however, the GLCM texture feature extraction always refers to multi/hyperspectral images. The widely used strategy to deal with this issue is to calculate the GLCM from the first principal component or the panchromatic band, which do not include all the useful information. Accordingly, in this study, we propose to represent the multichannel textures for multi/hyperspectral imagery by the use of: (1) clustering algorithms; and (2) sparse representation, respectively. In this way, the multi/hyperspectral images can be described using a series of quantized codes or dictionaries, which are more suitable for multichannel texture representation than the traditional methods. Specifically, K-means and fuzzy c-means methods are adopted to generate the codes of an image from the clustering point of view, while a sparse dictionary learning method based on two coding rules is proposed to produce the texture primitives. The proposed multichannel GLCM textural extraction methods were evaluated with four multi/hyperspectral datasets: GeoEye-1 and QuickBird multispectral images of the city of Wuhan, the well-known AVIRIS hyperspectral dataset from the Indian Pines test site, and the HYDICE airborne hyperspectral dataset from the Washington DC Mall. The results show that both the clustering-based and sparsity-based GLCM textures outperform the traditional method (extraction based on the first principal component) in terms of classification accuracies in all the experiments.

show abstract

A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data

Cited by 272 publications

References 13 publications

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Forest type mapping using object-based classification method in Kapilvastu district, Nepal

A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation

Contact Info

Product

Resources

About