Modelling And Visualizing Uncertainties In Remote Sensing Information

The inherent uncertainties in the real world and the limitation of human cognition are primary factors of the abundance of uncertainty existing in the judgment of sciences to objectivities. Due to its universality and reality, the uncertainty problem is becoming a hotspot and a difficult issue in the present research fields.The research target in remote sensing is the spatial entity. An amount of uncertainties exist in the spatial data describing the spatial entity, including temporal-relative location data and attribute data, some of which may seriously distort the reliability of products. Data quality is the core of data, and the key of data quality is evaluating uncertainty.One of the key points in research of uncertainties in remote sensing is measuring and visualizing the degree and spatial distribution of uncertainty completely and accurately in the processing of remotely sensed image. In the classical fashions, e.g. error matrix and Kappa coefficient, the performance of the classification models is estimated directly on the training data. Whereas it is actually not appropriate. The error matrix based on the training data set can not be regarded as the measurement of overall accuracy of classification models, and these models' performance need to be evaluated on "out-of-sample-data" - data that have not been used in constructing the models. In this paper, we apply the rough sets theory as the application framework of measuring the attribute uncertainties in remote sensing information, and several measures are proposed for assessing the attribute uncertainties in sample area data and different spatial objects based on the scale of pixel, landcover class and the whole image in classified remotely sensed imagery. These measurements could measure effectively attribute uncertainties and facilitate to trace the propagation of error and uncertainties in classified remotely sensed data. Subsequently, the remotely sensed imagery of Landsat 5 TM located at the Yellow River Delta is utilized as a case study of uncertainty analysis.Remotely sensed images often contain a combination of both pure and mixed pixels. The presence of mixed pixels is primarily due to the raster encoding format of remotesensing data, characteristic of the sensor's response to radiation, spatial resolution of the sensor, and spatial scale. The uncertainty in the imagery resulted from the aforementioned reflects the inherent characteristic of uncertainty kept in the technique of remote sensing. The existence of mixed pixels restricts the accuracy of classical classifiers of remotely sensed information based on the level of pixel, and these classifiers rarely consider the relationship of spatial conjunction between the neighboring pixels in the whole image, in other words, the spatial dependency of spatial data or spatial autocorrelation. In this paper, a novel simulating algorithm for identifying the spatial distribution of subpixels in a mixed pixel is proposed based on the assumption of spatial autocorrelation. Cooperating with the information on the proportions of every endmember component presented within a pixel derived from a soft classification, the algorithm simulates the spatial distribution of landcovers within the mixed pixel, and implements crossing through the pixel and identifying the boundary between the field objects at the subpixel scale. Subsequently, the performance and efficiency of the simulating algorithm is tested on different configuration of hardware platforms with the artificial imagery and synthetic imagery respectively.Large amount of uncertainties exist in the remote sensing information. If only mathematics measurement on the uncertainty degree of the remote sensing information is utilized, while the distribution characteristics in the temporal and spatial domains is ignored, the uncertainties in remote sensing information will be difficult to be completely and accurately described and understood. Representing uncertainty information from the point of view of visual sensation is an important section of modelling uncertainty. The uncertainty visualizing technology will help the users to explore uncertainty of the raw data and the size, distribution, special structure and tendency of the uncertainty. Then, the existence of uncertainty in data and the effects of uncertainty on the final decision would be simply and exactly felt by the users and products of remote sensing can be better understood and employed. In this paper, methods and technologies of uncertainty visualization are discussed and applied by the compartmentalization of static and dynamic visualized variable and feature visualization. The technique of the parallel coordinates is applied with emphasis in the feature space of remote sensing information to visualize the imagery's character of spectral and uncertainty, and then some visualizing examples of uncertainty measurements in the classified remotely sensed imagery are provided.At last, the design and the flow of data handling of the software UnVis for analyzing uncertainty in the remote sensing information are described, and the performance of the two finished functional modules of attribute uncertainty measuring and subpixel spatial distribution simulating is tested.