Abstract
Maximum Likelihood Classification: MLC based on classified result of boundary Mixed Pixels (Mixel) for high spatial resolution of remote sensing satellite images is proposed and evaluated with Landsat Thematic Mapper: TM images. Optimum threshold indicates different results for TM and Multi Spectral Scanner: MSS data. This may since the TM spatial resolution is 2.7 times finer than MSS, and consequently, TM imagery has more spectral variability for a class. The increase of the spectral heterogeneity in a class and the higher number of channels being used in the classification process may play significant role. For example, the optimum threshold for classifying an agricultural scene using MSS data is about 2.5 standard deviations, while that for TM corresponds to more than four standard deviations. This paper compares the optimum threshold between MSS and TM and suggests a method of using unassigned boundary pixels to determine the optimum threshold. Further, it describes the relationship of the optimum threshold to the class variance with a full illustration of TM data. The experimental conclusions suggest to the user some systematic methods for obtaining an optimal classification with MLC.
Highlights
A day, high spatial resolution of remote sensing satellite imagery data is available
Variance of a designated class in a feature space is increased in accordance with the spatial resolution which result in poor classification performance
The optimal threshold decision method proposed in this paper extracts boundary pixels in different class regions, performs maximum likelihood classification with a certain threshold on those pixels, and determines the number of pixels classified into unset classes It is optimized with a threshold value that is half the number of pixels
Summary
High spatial resolution of remote sensing satellite imagery data is available. Variance of a designated class in a feature space is increased in accordance with the spatial resolution which result in poor classification performance Another disadvantage of the high spatial resolution of satellite image classification is determination of optimum threshold for the discrimination between classes in the well-known Maximum Likelihood classification or some other classification methods such as Support Vector Machine, Deep Learning Based Method, etc. The optimal threshold decision method proposed in this paper extracts boundary pixels in different class regions, performs maximum likelihood classification with a certain threshold on those pixels, and determines the number of pixels classified into unset classes It is optimized with a threshold value that is half the number of pixels. The proposed method is explained, and it is shown using TM data that it matches the optimal threshold value obtained by the conventional method, and the validity of the proposed method is shown
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