M.L.F. Velloso

Assessment of Binary Coding Techniques for Texture Characterization in Remote Sensing Imagery

This letter investigates the use of rotation invariant descriptors based on Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) for texture characterization in the context of land-cover and land-use classification of Remote Sensing (RS) optical image data. Very high resolution images from the IKONOS-2 and Quickbird-2 orbital sensor systems covering different urban study areas were subjected to classification through an object-based approach. The experiments showed that the discrimination capacity of LBP and LPQ descriptors substantially increased when combined with contrast information. This work also proposes a novel texture descriptors assembled through the concatenation of the histograms of either LBP or LPQ descriptors and of the local variance estimates. Experimental analysis demonstrated that the proposed descriptors, though more compact, preserved the discrimination capacity of bi-dimensional histograms representing the joint distribution of textural descriptors and contrast information. Finally, the paper compares the discrimination capacity of the LBP- and LPQ-based textural descriptors with that of features derived from the Gray Level Co-occurrence Matrices (GLCM). The related experiments revealed a noteworthy superiority of LBP and LPQ descriptors over the GLCM features in the context of RS image data classification.

Improved radiometric normalization for land cover change detection: an automated relative correction with artificial neural network

Digital change detection methods have been broadly divided into either pre-classification spectral change detection or post-classification change detection. Since all spectral change detection methods are based on pixel-wise plus operations or scene-wise plus pixel-wise operations, accuracy in image registration and scene-to-scene radiometric normalization is more critical for these methods than for other methods. A wide range of algorithms has been developed to adjust linear models. This paper proposes an automated radiometric normalization process that uses an artificial neural network to adjust a non-linear mapping to minimize the effects of the influences of radiometric differences on image interpretation and classification.

Unsupervised change detection using fuzzy entropy principle

This paper presents an unsupervised change detection method for computing the amount of changes that have occurred within an area by using remotely sensed technologies and fuzzy modeling. The discussion concentrates on the formulation of a standard procedure that, using the concept of fuzzy sets and fuzzy logic, can define the likelihood of changes detected from remotely sensed data. The fuzzy visualization of areas undergoing changes can be incorporated into a decision support system for prioritization of areas requiring environmental monitoring. We propose an automatic technique for the analysis of the difference image. Such technique allows the automatic selection of the decision threshold. We used a thresholding approach by performing fuzzy partition on an n-dimensional histogram, which included contextual information, based on fuzzy relation and maximum fuzzy entropy principle. Experimental results confirm the effectiveness of proposed technique.

Change-detection of land cover using fuzzy sets and remotely sensed data

This work present a technique for computing land cover changes by using remotely sensed technologies and fuzzy modeling. In order to assess the effectiveness of this technique, we carried out experiments on two different data sets: one was a real multitemporal data set composed of two multispectral images acquired by the Thematic Mapper sensor of the Landsat 7 satellite of a region in Brazil under severe degradation process; the other was a synthetic data set generated to evaluate the robustness of the used technique against the use of noise.

Pattern Spectra for Texture Segmentation of Gray-Scale Images

This paper presents an unsupervised segmentation of textured images which combines local pattern spectra features and dimensionality reduction techniques. A pattern spectrum is a shape-size descriptor which can detect critical scales in an image and quantify various aspects of its shape-size content. We estimated local features from pattern spectra for discrete graytone images and arbitrary multilevel signals by using a discrete-size family of patterns. Then we applied dimensionality reduction techniques on the features extracted for achieving redundancy reduction and noise reduction. Recently, many neural algorithms have proposed for principal component analysis (PCA) and independent component analysis. In this work, we used two neural PCA and two neural ICA algorithms and compared them.

Fuzzy Modeling to Forecast an Electric Load Time Series

Abstract This paper tests and compares two types of modelling to predict the same time series. A time series of electric load was observed and, as a case study, we opted for the metropolitan region of Bahia State. The combination of three exogenous variables were attempted in each model. The exogenous variables are: the number of customers connected to the electricity distribution network, the temperature and the precipitation of rain. The linear model time series forecasting used was a SARIMAX. The modelling of computational intelligence used to predict the time series was a Fuzzy Inference System. According to the evaluation of the attempts, the Fuzzy forecasting system presented the lowest error. But among the smallest errors, the results of the attempts also indicated different exogenous variables for each forecast model.

Competitive artificial neural network for change-detection of land cover: an unsupervised approach

This work investigates the potential of an unsupervised network classifier, the Centroid Neural Network (CNN), for land cover change detection in remotely sensed images. Experiments carried out to evaluate the algorithm include change detection in both approaches: pre-classification and post-classification. Results confirm the effectiveness of this technique.

Non-parametric smoothing for relative radiometric correction on remotely sensed data

Digital change detection methods have been broadly divided into either pre-classification spectral change detection or post-classification change detection. Since all spectral change detection methods are based on pixel-wise operations, or scene-wise plus pixel-wise operations, accuracy in image registration and scene-to-scene radiometric normalization is more critical for these methods than for other methods. A wide range of algorithms has been developed to adjust linear models. This paper proposes an automated radiometric normalization process that automatically extracts the training dataset, and uses a non-parametric smoother to adjust a non-linear mapping in order to minimize the effects of the influences of radiometric differences on image interpretation and classification. In order to investigate how the proposed normalization improves the performance classification, and assess the effectiveness of this technique, we carried out classification experiments on three image sets, and compare their results.

Relative radiometric correction on remotely sensed data for land cover change detection: an unsupervised clustering approach

This paper proposes and automated radiometric normalization process that uses an unsupervised clustering to adjust a cluster-by-cluster mapping to minimize the effects of the influences of radiometric difference on image interpretation and classification.

Change-Detection Using Contextual Information and Fuzzy Entropy Principle

This paper presents an unsupervised change detection method for computing the amount of changes that have occurred within an area by using remotely sensed technologies and fuzzy modeling. The discussion concentrates on the formulation of a standard procedure that, using the concept of fuzzy sets and fuzzy logic, can define the likelihood of changes detected from remotely sensed data. The fuzzy visualization of areas undergoing changes can be incorporated into a decision support system for prioritization of areas requiring environmental monitoring. One of the main problems related to unsupervised change detection methods lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both, the accuracy and the reliability of the change-detection process. To overcome such drawbacks, in this paper, we propose an automatic technique for the analysis of the difference image. Such technique allows the automatic selection of the decision threshold. We used a thresholding approach by performing fuzzy partition on a two-dimensional (2-D) histogram, which included contextual information, based on fuzzy relation and maximum fuzzy entropy principle. Experimental results confirm the effectiveness of proposed technique.