Housing quality in urban areas - Data acquisition and classification through the analysis of remote sensor imagery
Color infrared aerial photography for acquiring and classifying data on urban housing quality
Horton, F. E.↗
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Feature extraction and classification algorithms for high dimensional data
Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.
Lee, Chulhee↗
Comparison of MSS and TM Data for Landcover Classification in the Chesapeake Bay Area: a Preliminary Report
An area bordering the Eastern Shore of the Chesapeake Bay was selected for study and classified using unsupervised techniques applied to LANDSAT-2 MSS data and several band combinations of LANDSAT-4 TM data. The accuracies of these Level I land cover classifications were verified using the Taylor's Island USGS 7.5 minute topographic map which was photointerpreted, digitized and rasterized. The the Taylor's Island map, comparing the MSS and TM three band (2 3 4) classifications, the increased resolution of TM produced a small improvement in overall accuracy of 1% correct due primarily to a small improvement, and 1% and 3%, in areas such as water and woodland. This was expected as the MSS data typically produce high accuracies for categories which cover large contiguous areas. However, in the categories covering smaller areas within the map there was generally an improvement of at least 10%. Classification of the important residential category improved 12%, and wetlands were mapped with 11% greater accuracy.
Mulligan, P. J.↗
Improvements in forest classification and inventory using remotely sensed data
A Forest Classification and Inventory System (Focis) has been developed for large area forest inventories on the basis of Landsat and digital terrain data. It is a potential advantage of Focis that it can provide timely inventories at a reduced cost which are easily updated. The Klamath National Forest in Northern California was employed as test area for the initial development of Focis. Focis is constantly being changed and improved. Two recent additions to the inventory system include a spatial filtering algorithm which improves the spatial coherence in the final classified image, and a modification to the classification procedure designed to reduce the adverse effects of local topography on classification accuracy. Attention is given to a Focis overview, spatial filtering, the interface with the forest service geographic information system, and efforts to reduce the influence of topography.
Woodcock, C. E.↗
A method of classification for multisource data in remote sensing based on interval-valued probabilities
An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method.
Kim, Hakil↗
Classification of ERTS-1 MSS data by canonical analysis
Classification of ERTS-1 MSS data by canonical analysis
Lachowski, H. M.↗
Unsupervised classification of remote multispectral sensing data
The new unsupervised classification technique for classifying multispectral remote sensing data which can be either from the multispectral scanner or digitized color-separation aerial photographs consists of two parts: (a) a sequential statistical clustering which is a one-pass sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. Applications of the technique using an IBM-7094 computer on multispectral data sets over Purdue's Flight Line C-1 and the Yellowstone National Park test site have been accomplished. Comparisons between the classification maps by the unsupervised technique and the supervised maximum liklihood technique indicate that the classification accuracies are in agreement.
Su, M. Y.↗
Evaluation of several schemes for classification of remotely sensed data
Various numerical analysis schemes for the classification of remotely sensed data are evaluated with respect to their capabilities for crop identification. A per point Gaussian maximum likelihood classifier, per point sum-of-normal-densities classifier, per point linear classifier, per point Gaussian maximum likelihood decision tree classifier and a texture-sensitive per field Gaussian maximum likelihood classifier were applied to seven sets of Landsat MSS data on several crop types and regions. The results of the implementation of the classifiers indicate that, given a representative set of training statistics, the choice of classification algorithm of the differentiation of corn and soybeans from one another and from other crop types made relatively little difference in accuracy, whereas the use of a different training method affected the accuracy significantly. In addition, the linear classifier is found to be the easiest for the analyst to use and to cost least in computer time per classification.
Hixson, M.↗
African land-cover classification using satellite data
Data from the advanced very high resolution radiometer sensor on the National Oceanic and Atmospheric Administration's operational series of meteorological satellites were used to classify land cover and monitor vegetation dynamics for Africa over a 19-month period. There was a correspondence between seasonal variations in the density and extent of green leaf vegetation and the patterns of rainfall associated with the movement of the Intertropical Convergence Zone. Regional variations, such as the 1983 drought in the Sahel of western Africa, were observed. Integration of the weekly satellite data with respect to time for a 12-month period produced a remotely sensed estimate of primary production based upon the density and duration of green leaf biomass. Eight of the 21-day composited data sets covering an 11-month period were used to produce a general land-cover classification that corresponded well with those of existing maps.
Tucker, C. J.↗
Procedure 1 and forestland classification using Landsat data
Procedure 1 (P-1) was developed for the Large Area Crop Inventory Experiment (LACIE) and has been used extensively to develop land-use classification of agricultural areas. The P-1 approach requires that pixels (also called dots) of known identity must be located in the study scene. The entire area is clustered and the spectral classes formed are identified using the dots. The analyst need only locate and identify the dots. The rest of the work is done by the computer. The objective of the reported study was to evaluate the effectiveness of P-1's automated approach in a complex forest-land situation. The study site was located in the eastern half of the San Juan National Forest in southwestern Colorado. The study showed that P-1 performed as well as the Multicluster Blocks approach on the rugged study area.
Nelson, R. F.↗
Land use classification for hydrologic models using interactive machine classification of LANDSAT data
Models designed to simulate the hydrology of urban areas require input parameters describing the land use and degree of imperviousness of the watershed. Unfortunately, the magnitude and spatial distribution of these parameters are rather difficult to estimate when a large watershed is involved. Trade-offs between accuracy of the model parameters and the time or money available for their determination must be made. Because of the necessity of such trade-offs, a study was developed to investigate the use of computer aided analysis of LANDSAT multispectral data in estimating percent of imperviousness and associated land uses needed in urban hydrologic modeling. An interactive computer was used to delineate seven land use classifications in the 342 sq. km. Maryland portion of the Anacostia River Basin from LANDSAT data. These results compared favorably with those of an earlier study which obtained the same information through analysis of aerial photographs having a scale of 1:4800. Approximately 94 man days were required to complete the land use analysis using the aerial photographs while less than three man days were required to accomplish similar tasks using the LANDSAT data.
Thomas J. Jackson↗
Significant techniques for the processing and interpretation of ERTS-1 data
Preprocessing, classification, and correlation of ERTS-1 data with ground features and analysis of spectral clustered results
Cousin, S. B.↗
Characteristics of playa deposits as seen on SIR-A, Seasat and Landsat coregistered data
A classification technique currently under development has been applied for the qualitative study of a playa located in Northeastern Algeria using coregistered SIR-A, Seasat and Landsat MSS 7 data. This classification which is texture based can be applied to only single band imagery at this time. The first results are very encouraging and reach conclusions similar to those obtained with more time consuming classifiers. The signature of each class is well expressed for the Landsat picture. Misclasifications occur with the SIR-A or Seasat images.
Rebillard, PH.↗
The effect of atmospheric water vapor on automatic classification of ERTS data
Absorption by atmospheric water vapor changes the spectral signatures collected by multispectral scanners if channels are not chosen to avoid the atmospheric water bands. For ERTS (Earth Resources Technology Satellite), the Multispectral Scanner band 7 (MSS 7, .8 to 1.1 micron) is the only band significantly affected. Line-by-line atmospheric absorption calculations showed that this effect can multiply the intensity by factors ranging from .77 to 1.0. If horizontal gradients in atmospheric water exist between training fields and the rest of the scene, errors are introduced in automatic classification of the imagery. The degradation of the classification of corn and soybeans was determined by using actual ERTS data and simulating the absorption effects on the MSS 7 band.
Pitts, D. E.↗
The Potential of Using Landsat 7 Data for the Classification of Sea Ice Surface Conditions During Summer
During spring and summer, the Surface of the Arctic sea ice cover undergoes rapid changes that greatly affect the surface albedo and significantly impact the further decay of the sea ice. These changes are primarily the development of a wet snow cover and the development of melt ponds. As melt pond diameters generally do not exceed a couple of meters, the spatial resolutions of sensors like AVHRR and MODIS are too coarse for their identification. Landsat 7, on the other hand, has a spatial resolution of 30 m (15 m for the pan-chromatic band). The different wavelengths (bands) from blue to near-infrared offer the potential to distinguish among different surface conditions. Landsat 7 data for the Baffin Bay region for June 2000 have been analyzed. The analysis shows that different surface conditions, such as wet snow and meltponded areas, have different signatures in the individual Landsat bands. Consistent with in-situ albedo measurements, melt ponds show up as blueish whereas dry and wet ice have a white to gray appearance in the Landsat true-color image. These spectral differences enable the distinction of melt ponds. The melt pond fraction for the scene studied in this paper was 37%.
Markus, Thorsten↗
Geospatial Method for Computing Supplemental Multi-Decadal U.S. Coastal Land-Use and Land-Cover Classification Products, Using Landsat Data and C-CAP Products
This paper discusses the development and implementation of a geospatial data processing method and multi-decadal Landsat time series for computing general coastal U.S. land-use and land-cover (LULC) classifications and change products consisting of seven classes (water, barren, upland herbaceous, non-woody wetland, woody upland, woody wetland, and urban). Use of this approach extends the observational period of the NOAA-generated Coastal Change and Analysis Program (C-CAP) products by almost two decades, assuming the availability of one cloud free Landsat scene from any season for each targeted year. The Mobile Bay region in Alabama was used as a study area to develop, demonstrate, and validate the method that was applied to derive LULC products for nine dates at approximate five year intervals across a 34-year time span, using single dates of data for each classification in which forests were either leaf-on, leaf-off, or mixed senescent conditions. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and C-CAP value-added products. Each classification's overall accuracy was assessed by comparing stratified random locations to available reference data, including higher spatial resolution satellite and aerial imagery, field survey data, and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall Kappa statistics ranging from 0.78 to 0.89. The accuracies are comparable to those from similar, generalized LULC products derived from C-CAP data. The Landsat MSS-based LULC product accuracies are similar to those from Landsat TM or ETM+ data. Accurate classifications were computed for all nine dates, yielding effective results regardless of season. This classification method yielded products that were used to compute LULC change products via additive GIS overlay techniques.
Spruce, J. P.↗
A unified approach to the classification of visual data systems.
Description of an approach to attaining a unified means of characterizing film, TV, and optical data systems. The concept is based on the premise that all of these imaging systems can be described by an equation similar to the ideal imaging system described by Rose (1948). This technique permits the direct comparison of film and TV performance without converting speed, film resolution lines, TV resolution lines, highlight output current and video bandwidth into compatible units, only to find some essential element of the conversion has been omitted from the particular specification in use. Most important, it permits system performance criteria to be based on input and output criteria without extensive manipulation of the elements between input and output.
Black, J.↗
Use of ERTS-1 data in identification, classification, and mapping of salt-affected soils in California
There are no author-identified significant results in this report.
Colwell, R. N.↗