Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division

Zhou, Wen; Ming, Dongping; Xu, Lu; Bao, Hanqing; Wang, Min

doi:10.1155/2018/3918954

Cited by 13 publications

(10 citation statements)

References 39 publications

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“…(4) e scale factor is dependent on the correspondence between spatial scale and object features itself, so it is less realistic to obtain an absolutely optimal scale suitable for all features on the image; however, it is a compromise to get a relatively optimal scale by using the proposed frequency spectrum statistics method. (5) When the images are mosaics of different classes or features because of the scale dependence of geoobject, stratified scale processing [3,59] is necessary. As is known, the same kind of objects often have the similar spatial scale and often cluster in a local area, so the image can be roughly divided into some local regions within which the same objects gather and then the proposed texture scale selection method can be used within each local regions.…”

Section: Discussionmentioning

confidence: 99%

“…High spatial resolution remote sensing images contain rich texture information, and accurate description of texture features can effectively distinguish complex land-cover category [1][2][3]. Traditional texture feature extraction algorithms can be classified into five categories [4]: structural model, statistic model (GLCM, local variance analysis, and semivariance analysis), filter model (Fourier transformation, wavelet transformation, and Gabor filters), random field (Gaussian-Markov), and fractal model.…”

Section: Introductionmentioning

confidence: 99%

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Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis

Cao

Ming

et al. 2019

Journal of Spectroscopy

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Image texture is an important visual cue in image processing and analysis. Texture feature expression is an important task of geo-objects expression by using a high spatial resolution remote sensing image. Texture features based on gray level co-occurrence matrix (GLCM) are widely used in image spatial analysis where the spatial scale is especially of great significance. Based on the Fourier frequency-spectral analysis, this paper proposes an optimal scale selection method for GLCM. Different subset textures are firstly upscaled by GLCM with different window sizes. Then the multiscale texture feature images are converted into the frequency domain by Fourier transform. Consequently, the radial distribution and angular distribution curves changing with different window sizes from spectrum energy can be achieved, by which the texture window size can be selected. In order to verify the validity of this proposed texture scale selection method, this paper uses high-resolution fusion images to classify land cover based on multiscale texture expression. The results show that the proposed method combining frequency-spectral analysis-based texture scale selection can guarantee the quality and accuracy of the classification, which further proves the effectiveness of optimal texture window size selection method bases on frequency spectrum analysis. Other than scale selection in spatial domain, this paper casts a novel idea for texture scale selection in the frequency domain, which is meant for scale processing of remote sensing image.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis

Cao

Ming

et al. 2019

Journal of Spectroscopy

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“…The classification technique does not consider the object-based image analysis for minimizing the feature extraction time. A stratified object-oriented technique was introduced in [16] for categorizing the remote sensing image and feature extraction. Though the technique segments the image objects, the error rate was not minimized.…”

Section: Related Workmentioning

confidence: 99%

Tanimoto Gaussian Kernelized Feature Extraction Based Multinomial Gentleboost Machine Learning for Multi-Spectral Aerial Image Classification

2019

IJITEE

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Aerial images provide a landscape view of earth surfaces that utilized to monitor the large areas. Each Aerial image comprises the different scenes to identify the objects on the digital maps. The several methodologies have been developed to solve the problem of the scene classification using input aerial images. The method does not improve the classification performance using more aerial images. In order to improve the classification performance, a Tanimoto Gaussian Kernelized Feature Extraction Based Multinomial GentleBoost Classification (TGKFE-MGBC) technique is introduced. The TGKFE-MGBC technique comprises three major processes namely object-based segmentation, feature extraction and aerial image scene classification. At first, object-based segmentation partitions the aerial image into several sub-bands. Aerial image with more than two objects is called as multi-spectral. The objects in spectral bands are identified by Tanimoto pixel similarity measure. This process helps to reduce the feature extraction time. Each object has different features like shape, size, color, texture and so on. After that, Gaussian Kernelized Feature Extraction is carried out to extracts the features from the objects with minimal time. Finally, the Multinomial GentleBoost Classification is applied for categorizing the scenes into different classes with the extracted features. The GentleBoost is an ensemble technique uses multinomial naïve Bayes probabilistic classifier as a weak learner and it combines to makes a strong one for classifying the scenes. The strong classifier result improves the aerial image scene classification accuracy and minimizes the false positive rate. Simulation is conducted using aerial image database with different factors such as feature extraction time, aerial image scene classification accuracy and false positive rate. The results showed that the TGKFE-MGBC technique effectively improves the aerial image scene classification accuracy and minimizes the feature extraction time as well as the false positive rate.

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“…Differently, Kavzoglu et al [32] applied a multi-resolution segmentation result to divide the image, which resulted in a better accuracy than undivided classification. Zhou et al [33] applied an image scene to divide the VHR images. Handling the local regional images (i.e., segmentation and classification) with local optimal parameters can effectively improve the accuracy of object extraction.…”

Section: Introductionmentioning

confidence: 99%

Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation

Ming

Zhou

et al. 2019

Remote Sensing

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Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction.

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Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division

Cited by 13 publications

References 39 publications

Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis

Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis

Tanimoto Gaussian Kernelized Feature Extraction Based Multinomial Gentleboost Machine Learning for Multi-Spectral Aerial Image Classification

Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation

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