Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method

TL;DR

This paper introduces an adaptive thresholding method for remote sensing image classification that improves robustness to illumination variations, simplifies implementation, and enhances feature selection for better accuracy and efficiency.

Contribution

The paper proposes a novel interactive adaptive thresholding technique that effectively handles illumination changes and integrates feature selection for improved remote sensing image classification.

Findings

Robust to illumination variations in remote sensing images

Simplifies image classification preprocessing

Enhances classification accuracy and efficiency

Abstract

Remote sensing image classification can be performed in many different ways to extract meaningful features. One common approach is to perform edge detection. A second approach is to try and detect whole shapes, given the fact that these shapes usually tend to have distinctive properties such as object foreground or background. To get optimal results, these two approaches can be combined. This paper adopts a combinatorial optimization method to adaptively select threshold based features to improve remote sensing image. Feature selection is an important combinatorial optimization problem in the remote sensing image classification. The feature selection method has to achieve three characteristics: first the performance issues by facilitating data collection and reducing storage space and classification time, second to perform semantics analysis helping to understand the problem, and third to improve prediction accuracy by avoiding the curse of dimensionality. The goal of this thresholding an image is to classify pixels as either dark or light and evaluation of classification results. Interactive adaptive thresholding is a form of thresholding that takes into account spatial variations in illumination of remote sensing image. We present a technique for remote sensing based adaptive thresholding using the interactive satellite image of the input. However, our solution is more robust to illumination changes in the remote sensing image. Additionally, our method is simple and easy to implement but it is effective algorithm to classify the image pixels. This technique is suitable for preprocessing the remote sensing image classification, making it a valuable tool for interactive remote based applications such as augmented reality of the classification procedure.