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Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery. [ABSTRACT FROM AUTHOR]

A remote sensing approach is proposed in this paper for monitoring instantaneous underwater light climate in Dahuchi Lake, China. Water constituents that are optically significant to the lake water can be retrieved through this method based on an artificial neural network (ANN) algorithm with a satisfactory accuracy. In this study, the ANN was trained by spectral simulations which were generated by a water colour simulation model (WASI). An underwater light attenuation model has been built by fitting the field measurements and applied to calculate photosynthetically available radiation (PAR) at the bottom of the lake from the inputs of hyperspectral reflectance above the water surface and the water depth. Results show that this method can predict the concentration of suspended particle matter (SPM) with a high accuracy and explain more than half of the underwater PAR variation with a standard deviation of 94.8 μmol s-1 m-2. In order to test the portability of the algorithm, it has also been extended to three bands of Landsat TM image. Compared with hyperspectral inputs, ANN with broadband inputs has lower but still acceptable accuracy on SPM retrieval. The advantage of this method lies in its time-efficient and cost-efficient monitoring of underwater light climate, i.e. the simulations generated by a semi-physical model can be used for ANN training, instead of using a large number of field measurements. [ABSTRACT FROM AUTHOR]

The local transfer function classifier (LTF-C) is a new radial basis function (RBF)-like neural network, but it uses an entirely different learning algorithm, so as to achieve the novel ability of locally partitioning the feature space. This paper investigates LTF-C and the RBF neural network with reference to land cover classification with and without an exhaustively defined set of classes using Landsat-5 TM data. Results indicate that LTF-C achieves higher accuracy, usually with fewer hidden units, than the RBF neural network with an exhaustively defined set of classes. LTF-C is more stable than the RBF neural network during classifications of the testing set, including the untrained class. Through the setting of post-classification thresholds on the network's outputs, a well-trained RBF neural network sometimes gives abnormally high output value for an input pattern which represents the untrained class. Meanwhile, a well-trained LTF-C outputs extremely low values all the time under the same circumstances. Therefore, LTF-C may outperform the RBF neural network in detecting or removing the atypical classes that are excluded from the training set, which maybe useful in situations where only interesting types of land cover are selected in the training set, due to high labour costs or difficulties in defining all classes represented in a study area. [ABSTRACT FROM AUTHOR]

This paper explores the potential of an artificial immune-based supervised classification algorithm for land-cover classification. This classifier is inspired by the human immune system and possesses properties similar to nonlinear classification, self/non-self identification, and negative selection. Landsat ETM+ data of an area lying in Eastern England near the town of Littleport are used to study the performance of the artificial immune-based classifier. A univariate decision tree and maximum likelihood classifier were used to compare its performance in terms of classification accuracy and computational cost. Results suggest that the artificial immune-based classifier works well in comparison with the maximum likelihood and the decision-tree classifiers in terms of classification accuracy. The computational cost using artificial immune based classifier is more than the decision tree but less than the maximum likelihood classifier. Another data set from an area in Spain is also used to compare the performance of immune based supervised classifier with maximum likelihood and decision-tree classification algorithms. Results suggest an improved performance with the immune-based classifier in terms of classification accuracy with this data set, too. The design of an artificial immune-based supervised classifier requires several user-defined parameters to be set, so this work is extended to study the effect of varying the values of six parameters on classification accuracy. Finally, a comparison with a backpropagation neural network suggests that the neural network classifier provides higher classification accuracies with both data sets, but the results are not statistically significant. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm to estimate the onset of seasonal snow-melt using space-borne microwave radiometer data. We have earlier developed a simple model called a Channel Difference Algorithm (CDA) to estimate the beginning of the snow-melt. The new algorithm, the SOM Detection Algorithm (SDA), is based on the use of an artificial neural network system a called Self-Organizing Map (SOM). The purpose of this research is to develop a robust and simple algorithm feasible for operative use. The algorithm is tested using SSM/I data with hydrological predictions as reference data. The reference data covers two winters, 1997 and 1998, and is for the boreal forest zone in Finland. The results are promising. The SDA is able to estimate the beginning of the final snow-melt well, especially if the snow water equivalent exhibits large values. Using low-pass filtering for the SDA estimated time series, the estimation can be improved. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying ANNs, including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. The limitations and crucial issues relating to the application of the neural network approach are discussed. A brief review of the implementation of ANNs in some of the most popular image processing software packages is presented. Finally, we discuss the application perspectives of neural networks in remote sensing image analysis. [ABSTRACT FROM AUTHOR]

This paper introduces a new neural network, called the local transfer function classifier (LTF-C), for classification of multi-spectral remote sensing data. The network structure of LTF-C is similar to that of the radial basis function neural network (RBF), but LTF-C utilizes an entirely different learning algorithm. In particular, the network structure of LTF-C is not predetermined, but changes dynamically during the learning. Such a learning algorithm fits well to the classification problem, and guarantees that the size of the network is as large as is needed. The classification results show that LTF-C evidently has a better classification accuracy than the six other classifiers in the experiment. [ABSTRACT FROM AUTHOR]

Crop yield forecasting is a very important task for researchers in remote sensing. Problems exist with traditional statistical modelling (especially regression models) of nonlinear functions with multiple factors in the cropland ecosystem. This paper describes the successful application of an artificial neural network in developing a model for crop yield forecasting using back-propagation algorithms. The model has been adapted and calibrated using on the ground survey and statistical data, and it has proven to be stable and highly accurate. [ABSTRACT FROM AUTHOR]