A Hierarchical Framework for the Classification of Multispectral Imagery

AbstractOut of the abundant digital image data available, multispectral imagery is one which gives us information about the earth we live in. To gain knowledge from multispectral imagery, it is essential to classify the data present in the image based on spectral information. Classification plays a significant role in understanding the remotely sensed data obtained from the satellites. This paper brings out a new classification scheme based on a hierarchical framework. The hierarchical model proposed in this paper helps to understand the imagery at different levels of abstractness and concreteness to serve different applications like town planning, facility management and so on. The model depicts classification of the multispectral imagery on three abstract levels. The algorithm proposed outputs classification at different levels with an average accuracy of 72.6% in level 1 and 78.3% in level 2. The time sensitivity analysis of the algorithm shows that it outperforms the traditional SVM classifier. A detailed analysis of the algorithm proposed is detailed in this paper with respect to the parameters influencing the classification accuracy

Bag of ARSRG Words (BoAW)

In recent years researchers have worked to understand image contents in computer vision. In particular, the bag of visual words (BoVW) model, which describes images in terms of a frequency histogram of visual words, is the most adopted paradigm. The main drawback is the lack of information about location and the relationships between features. For this purpose, we propose a new paradigm called bag of ARSRG (attributed relational SIFT (scale-invariant feature transform) regions graph) words (BoAW). A digital image is described as a vector in terms of a frequency histogram of graphs. Adopting a set of steps, the images are mapped into a vector space passing through a graph transformation. BoAW is evaluated in an image classification context on standard datasets and its effectiveness is demonstrated through experimental results compared with well-known competitors