Abstract
Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users’ actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt–pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data.
Highlights
Sensed data, especially for satellite images, are used to estimate information about the Earth, its various objects, phenomena, and processes
In the case of salt–pepper noise, the pixel location may possibly be disturbed by the noise more than once, i.e., the first location of the seven-band satellite image data consists of seven pixels, and the noise can either be added or not added to a pixel, some pixels, or all pixels
The OAV0 for image classification with added speckle noise at 10 dB is 73.8% (K = 0.52), which differs considerably from the lowest accuracy obtained for the zero-mean Gaussian noise; the construction class still cannot be classified until a peak signal-to-noise ratios (PSNRs) of 30 dB is reached
Summary
Especially for satellite images, are used to estimate information about the Earth, its various objects, phenomena, and processes These images have been widely used for applications of Earth surface monitoring such as land cover classification and change detection, crop yield estimation, and geographic information extraction. Improving the accuracy of the classifications is a fundamental research topic in the field of geographic information sciences [1] It can be a difficult task depending on the complexity of the landscape, the spatial and spectral resolution of the imagery being used, and the amount of noise included. The image generation process may add noise to the data This is widely found in most Synthetic-aperture radar (SAR) images [5,6,7]. These data have to be compressed to reduce their requirements for archiving and data transmission and this may cause artifacts and ambiguities in the final images [8]
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