Image Classification Using Humps of Histogram

Classification techniques classify the remotely sensed image by using reflectance properties of pixels. This paper presents a new approach to classify multispectral remotely sensed image. This approach classifies the multispectral image using frequencies of spectral bands' grey level values (DN values) in Histogram. It draws histogram for different spectral bands of the image. Then, it finds and separates the humps in histograms. This approach yields more meaningful classification for multi-modal or bi-modal histograms. It creates 3 potential centroids in each hump for each spectral band. More the number of humps, more would be potential centroids for classification. Different spectral bands have different peaks in their humps of histograms. It reads all the pixels of one peak of one band and draw the local histogram of other bands' grey level values using pixels read. This way, peak of one hump of one band can find corresponding peaks in local histogram and these peaks make a pixel that can be a potential centroid and some of these peak frequencies is the actual frequency of that centroid. Now, I choose extreme left and extreme right grey level values whose frequency is greater than or equal to the average frequency of that hump. As each hump of each spectral band has three grey level values, I can find three centroids for each hump of each spectral band. Duplicate centroids are eliminated from the list of centroids. The rest of the centroids are recursively iterated and centroids with lesser frequencies than the nearby centroids are eliminated. Later, algorithm uses gravitational force to find out two nearby centroids.