CIELab Color Moments: Alternative Descriptors for LANDSAT Images Classification System

This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors. It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model. The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively. Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images

CIELab Color Moments: Alternative Descriptors for LANDSAT Images Classification System

This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors.  It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model.  The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively.  Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images.This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors.  It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model.  The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively.  Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images

Automatic learning of structural knowledge from geographic information for updating land cover maps

International audienceThe number of satellites and remote sensing sensors devoted to earth observation becomes increasingly high, providing more and more data and especially images. In the same time the access to such a data and to the tools to process them has been considerably improved. In the presence of such data flow - and regarding the necessity to follow up and predict environmental and societal changes in highly dynamic socio-environmental contexts - we need automatic image interpretation methods. This could be accomplished by exploring some strengths of artificial intelligence. Our main idea consists in inducing classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system. We applied our proposed methodology to three land cover/use maps of the French Guiana littoral. One hundred and forty six classification rules were induced for the 39 land-cover classes of the maps. These rules are expressed in first order logic language which make them intelligible and interpretable by non-experts. A ten-fold cross validation gave average values for classification accuracy, specificity and sensibility equal to, respectively, 98.82 %, 99.65% and 70%. The proposed methodology could be valuably exploited to automatically classify new objects and/or help operators using object-based classification procedures

Extraction de détecteurs d'objets urbains à partir d'une ontologie

National audienceAfin de parvenir à une méthode d'interprétation automatique d'im-ages de télédétection à très haute résolution spatiale, il est nécessaire d'exploiter autant que possible les connaissances du domaine. Pour détecter différents types d'objet comme la route ou le bâti, des méthodes très spécifiques ont été dévelop-pées pour obtenir de très bons résultats. Ces méthodes utilisent des connais-sances du domaine sans les formaliser. Dans cet article, nous proposons tout d'abord de modéliser la connaissance du domaine de manière explicite au sein d'une ontologie. Ensuite, nous introduisons un algorithme pour construire des détecteurs spécifiques utilisant les connaissances de cette ontologie. La sépara-tion nette entre modélisation des connaissances et construction des détecteurs rend plus lisible le processus d'interprétation. Ce découplage permet également d'utiliser l'algorithme de construction de détecteurs dans un autre domaine d'ap-plication, ou de modifier l'algorithme de construction de détecteurs sans modi-fier l'ontologie

Identifying Smokestacks in Remotely Sensed Imagery via Deep Learning Algorithms

Locating smokestacks in remote sensing imagery is a crucial first step to calculating smokestack heights, which allows for the accurate modeling of dioxin pollution spread and the study of resulting health impacts. In the interest of automating this process, this thesis examines deep learning networks and how changes in input datasets and network architecture affect image detection accuracy. This initial image detection serves as the first step in automated object recognition and height calculation. While this is applicable to general land use classification, this study specifically addresses detecting smokestack images. Different dataset scenarios are generated from the massive Functional Map of the World dataset, ranging from two to sixty-two classes, and network architectures from recent studies are used. Each dataset and network is analyzed in their performance by way of F-measure. Image characteristics are also analyzed from images that were correctly/incorrectly labeled by the algorithms, providing answers on what images the algorithms best predict and what qualities the algorithms cannot discern. The smokestack’s accuracy is reported at its highest through a five class training dataset, using an Adam Optimizer over six epochs. More or less classes returned lower scores, as did using the Stochastic Gradient Descent optimizer. Extended epochs did not return significantly higher or lower scores. The study concludes that while using more data can be effective in creating more accurate algorithms, using less data which is better structured for the problem at hand can have a greater effect

Leveraging machine learning to extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): a case study in forest-type mapping

Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy