A Simple Data Preprocessing and Postprocessing Techniques for SVM Classifier of Remote Sensing Multispectral Image Classification

Present scenario of the remote sensing domain deals with how to utilize the data for different purposes like classification, target detection, disaster management, change detection, flood monitoring, deforestation, etc. Now due to improvements in the sensor technology very high spatial and spectral resolutions data are available. Over a decade, various new advanced research papers have been projected in the literature for spatial and spectral classification of such high-resolution remote sensing images. Thematic information investigation of the earth's surface image is possible by the classification technique and the most frequently used method for this purpose is multispectral classification using a supervised learning process. In the supervised learning process, the specialist challenges to discover exact sites in the remotely sensed data that represent homogeneous examples of the known land cover type. The most recommended method for the classification of remote sensing (RS) images is the support vector machine (SVM) because of its high accuracy but any classifier depends on good quality training samples. The collection of authentic training samples of different classes is a critical issue when the whole classification result is important. This article presents a preprocessing technique based on local statistics for generation-correction of training samples with quadrant division. A simple filter-based postprocessing technique is proposed for the improvement of classification accuracy. We study rigorously how the proposed preprocessing technique has affected the result of classification accuracy for different kernels SVM classifiers. Also, we have presented the comparison results between the proposed method and other different classifiers in the literature.

Automated summary

This article discusses the classification of remote sensing images and proposes a preprocessing technique based on local statistics and a postprocessing technique using a simple filter to improve classification accuracy. The results of the proposed method are compared with other classifiers.