  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.
