Hugh G. Lewis

Super-Resolution Land Cover Pattern Prediction Using a Hopfield Neural Network

Landscape pattern represents a key variable in management and understanding of the environment, as well as driving many environmental models. Remote sensing can be used to provide information on the spatial pattern of land cover features, but analysis and classification of such imagery suffers from the problem of class mixing within pixels. Soft classification techniques can estimate the class composition of image pixels. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field-of-view (IFOV) represented by the pixel. Techniques to provide an improved spatial representation of land cover targets larger than the size of a pixel have been developed. However, the mapping of subpixel scale land cover features has yet to be investigated. We recently described the application of a Hopfield neural network technique to super-resolution mapping of land cover features larger than a pixel, using information of pixel composition determined from soft classification, and now show how our approach can be extended in a new way to predict the spatial pattern of subpixel scale features. The network converges to a minimum of an energy function defined as a goal and several constraints. Prior information on the typical spatial arrangement of the particular land cover types is incorporated into the energy function as a semivariance constraint. This produces a prediction of the spatial pattern of the land cover in question, at the subpixel scale. The technique is applied to synthetic and simulated Landsat Thematic Mapper (TM) imagery, and compared to results of an existing super-resolution target identification technique. Results show that the new approach represents a simple, robust, and efficient tool for super-resolution land cover pattern prediction from remotely sensed imagery.

Support vector machines for optimal classification and spectral unmixing

Mixture modelling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve the sub-pixel, mixture information, which arises from the overlapping land cover types within the pixel’s instantaneous field of view. This paper describes an approach based on a relatively new technique, support vector machines (SVMs), and contrasts this with more established algorithms such as linear spectral mixture models (LSMM) and artificial neural networks (ANN). In the simplest case, it is shown that the mixture regions formed by the linear support vector machine and the linear spectral mixture model are equivalent; however, the support vector machine automatically selects the relevant pure pixels. When non-linear algorithms are considered it can be shown that the non-linear support vector machines have model spaces which contain many of the conventional neural networks, multi-layer perceptrons and radial basis functions. However, the non-linear support vector machines automatically determine the relevant set of basis functions (nodes) from the performance constraints specified via the loss function and in doing so select only the data points which are important for making a decision. In practice, it has been found that only about 5% of the training exemplars are used to form the decision boundary region, which represents a considerable compression of the data and also means that validation effort can be concentrated on just those important data points.

Superresolution mapping using a hopfield neural network with fused images

Superresolution mapping is a set of techniques to increase the spatial resolution of a land cover map obtained by soft-classification methods. In addition to the information from the land cover proportion images, supplementary information at the subpixel level can be used to produce more detailed and accurate land cover maps. The proposed method in this research aims to use fused imagery as an additional source of information for superresolution mapping using the Hopfield neural network (HNN). Forward and inverse models were incorporated in the HNN to support a new reflectance constraint added to the energy function. The value of the function was calculated based on a linear mixture model. In addition, a new model was used to calculate the local endmember spectra for the reflectance constraint. A set of simulated images was used to test the new technique. The results suggest that fine spatial resolution fused imagery can be used as supplementary data for superresolution mapping from a coarser spatial resolution land cover proportion imagery.

Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for mapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale.

Superresolution mapping using a Hopfield neural network with lidar data

Superresolution mapping is a set of techniques to obtain a subpixel map from land cover proportion images produced by soft classification. Together with the information from the land cover proportion images, supplementary information at the subpixel level can be used to produce more detailed and accurate land cover maps. This research aims to use the elevation data from light detection and ranging (lidar) as an additional source of information for superresolution mapping using the Hopfield neural network (HNN). A new height function was added to the energy function of the HNN for superresolution mapping. The value of the height function was calculated for each subpixel of a certain class based on the Gaussian distribution. A set of simulated data was used to test the new technique. The results suggest that 0.8-m spatial resolution digital surface models can be combined with optical data at 4-m spatial resolution for superresolution mapping.

Multiple-class land-cover mapping at the sub-pixel scale using a Hopfield neural network

Land cover class composition of image pixels can be estimated using soft classification techniques. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Robust techniques to provide an improved spatial representation of land cover have yet to be developed. The use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification was investigated in previous papers by Tatem et al. The network converges to a minimum of an energy function defined as a goal and several constraints. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. Tatem et al described the application of the technique to target mapping at the sub-pixel scale, but only for single classes. We now show how this approach can be extended to map multiple classes at the sub-pixel scale, by adding new constraints into the energy formulation. The new technique has been applied to simulated SPOT HRV and Landsat TM agriculture imagery to derive accurate estimates of land cover. The results show that this extension of the neural network now represents a simple efficient tool for mapping land cover and can deliver requisite results for the analysis of practical remotely sensed imagery at the sub pixel scale.

Super-resolution mapping using Hopfield Neural Network with panchromatic imagery

Land-cover proportions of mixed pixels can be predicted using soft classification. From the land-cover proportions, a hard land-cover map can be predicted at sub-pixel spatial resolution using super-resolution mapping techniques. It has been demonstrated that the Hopfield Neural Network (HNN) provides a suitable method for super-resolution mapping. To increase the detail and accuracy of the sub-pixel land-cover map, supplementary information at an intermediate spatial resolution can be used. In this research, panchromatic (PAN) imagery was used as an additional source of information for super-resolution mapping. Information from the PAN image was captured by a new PAN reflectance constraint in the energy function of the HNN. The value of the new PAN reflectance constraint was defined based on forward and inverse models with local end-member spectra and local convolution weighting factors. Two sets of simulated and degraded data were used to test the new technique. The results indicate that PAN imagery can be used as a source of supplementary information to increase the detail and accuracy of sub-pixel land-cover maps produced by super-resolution mapping from land-cover proportion images.

Integrating voice recognition into models of person perception

The results of one empirical study are presented to investigate whether voice recognition might profitably be integrated into a single IAC network for person perception. An identity priming paradigm was used to determine whether face perception and voice perception combined to influence one another. The results revealed within-modality priming of faces by prior presentations of faces, and of voices by prior presentation of voices. Critically, cross-modality priming was also revealed, confirming that the two modalities can be represented within a single system and can influence one another. These results are supported by the results of a simulation, and are discussed in terms of the theoretical development of IAC, and the benefits and future questions that arise from consideration of an integrated multimodal model of person perception.

Determination of spatial and temporal characteristics as an aid to neural network cloud classification

Previous studies of cloud classification from meteorological satellite imagery have shown that artificial neural networks (ANNs) perform as well as, or better than, statistical pattern recognition when multispectral features, supplemented with selected textural features, are used. These features, however, represent only a subset of the full range of features available in this imagery. Spatial characteristics based on the shape of clouds, and temporal characteristics, derived from image sequences, can be more direct pointers to cloud type. In this paper the methods for the determination of such parameters are described, some results are presented, and the effectiveness of the methods are discussed.

Incorporating uncertainty in land cover classification from remote sensing imagery

Abstract This article reports an investigation of the sources of uncertainty arising from land cover characteristics that affect the performance of area estimation models.