Classification accuracy increase using multisensor data fusion

The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.) but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network). This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to other established methods illustrates the advantage in the classification accuracy for many classes such as buildings, low vegetation, sport objects, forest, roads, rail roads, etc

A Neural Network Method for Land Use Change Classification, with Application to the Nile River Delta

Detecting and monitoring changes in conditions at the Earth's surface are essential for understanding human impact on the environment and for assessing the sustainability of development. In the next decade, NASA will gather high-resolution multi-spectral and multi-temporal data, which could be used for analyzing long-term changes, provided that available methods can keep pace with the accelerating flow of information. This paper introduces an automated technique for change identification, based on the ARTMAP neural network. This system overcomes some of the limitations of traditional change detection methods, and also produces a measure of confidence in classification accuracy. Landsat thematic mapper (TM) imagery of the Nile River delta provides a testbed for land use change classification methods. This dataset consists of a sequence of ten images acquired between 1984 and 1993 at various times of year. Field observations and photo interpretations have identified 358 sites as belonging to eight classes, three of which represent changes in land use over the ten-year period. Aparticular challenge posed by this database is the unequal representation of various land use categories: three classes, urban, agriculture in delta, and other, comprise 95% of pixels in labeled sites. A two-step sampling method enables unbiased training of the neural network system across sites.National Science Foundation (SBR 95-13889); Office of Naval Research (N00014-95-1-409, N00014-95-0657); Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-042

ARTMAP Neural Network Classification of Land Use Change

The ability to detect and monitor changes in land use is essential for assessment of the sustainability of development. In the next decade, NASA will gather high-resolution multi-spectral and multi-temporal data, which could be used for detecting and monitoring long-term changes. Existing methods are insufficient for detecting subtle long-term changes from high-dimensional data. This project employs neural network architectures as alternatives to conventional systems for classifying changes in the status of agricultural lands from a sequence of satellite images. Landsat TM imagery of the Nile River delta provides a testbed for these land use change classification methods. A sequence often images was taken, at various times of year, from 1984 to 1993. Field data were collected during the summer of 1993 at88 sites in the Nile Delta and surrounding desert areas. Ground truth data for 231 additional sites were determined by expert site assessment at the Boston University Center for Remote Sensing. The field observations are grouped into classes including urban, reduced productivity agriculture, agriculture in delta, desert/coast reclamation, wetland reclamation, and agriculture in desert/coast. Reclamation classes represent land use changes. A particular challenge posed by this database is the unequal representation of various land use categories: urban and agriculture in delta pixels comprise the vast majority of the ground truth data available in the database. A new, two-step training data selection method was introduced to enable unbiased training of neural network systems on sites with unequal numbers of pixels. Data were successfully classified by using multi-date feature vectors containing data from all of the available satellite images as inputs to the neural network system.National Science Foundation Graduate Fellowship; National Science Foundation (SBR 95-13889); Office of Naval Research (N00014-95-I-409, N00014-95-0657); Air Force Office of Scientific Research (F49620-0l-1-0397)

Near ground level sensing for spatial analysis of vegetation

Measured changes in vegetation indicate the dynamics of ecological processes and can identify the impacts from disturbances. Traditional methods of vegetation analysis tend to be slow because they are labor intensive; as a result, these methods are often confined to small local area measurements. Scientists need new algorithms and instruments that will allow them to efficiently study environmental dynamics across a range of different spatial scales. A new methodology that addresses this problem is presented. This methodology includes the acquisition, processing, and presentation of near ground level image data and its corresponding spatial characteristics. The systematic approach taken encompasses a feature extraction process, a supervised and unsupervised classification process, and a region labeling process yielding spatial information

Flame Detection for Video-based Early Fire Warning Systems and 3D Visualization of Fire Propagation

Early and accurate detection and localization of flame is an essential requirement of modern early fire warning systems. Video-based systems can be used for this purpose; however, flame detection remains a challenging issue due to the fact that many natural objects have similar characteristics with fire. In this paper, we present a new algorithm for video based flame detection, which employs various spatio-temporal features such as colour probability, contour irregularity, spatial energy, flickering and spatio-temporal energy. Various background subtraction algorithms are tested and comparative results in terms of computational efficiency and accuracy are presented. Experimental results with two classification methods show that the proposed methodology provides high fire detection rates with a reasonable false alarm ratio. Finally, a 3D visualization tool for the estimation of the fire propagation is outlined and simulation results are presented and discussed.The original article was published by ACTAPRESS and is available here: http://www.actapress.com/Content_of_Proceeding.aspx?proceedingid=73

Art Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data

A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on the fuzzy ARTMAP neural network, is developed. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for vegetation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, as well as back propagation neural networks and K Nearest Neighbor algorithms. ARTMAP dynamics are fast, stable, and scalable, overcoming common limitations of back propagation, which did not give satisfactory performance. Best results are obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. A prototype remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automatically constructs a minimal number of recognition categories to meet accuracy criteria. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-l-0409, N00014-95-0657

Satellite image classification and segmentation using non-additive entropy

Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis entropy on the pattern recognition and segmentation of coloured images obtained by satellites, via "Google Earth". By segmentation we mean split an image to locate regions of interest. Here, we discriminate and define an image partition classes according to a training basis. This training basis consists of three pattern classes: aquatic, urban and vegetation regions. Our numerical experiments demonstrate that the Tsallis entropy, used as a feature vector composed of distinct entropic indexes $q$ outperforms the standard entropy. There are several applications of our proposed methodology, once satellite images can be used to monitor migration form rural to urban regions, agricultural activities, oil spreading on the ocean etc.Comment: 4 pages, 5 figures, ICMSquare 201