Conference Presentations by Adarsh K Khare
Classification techniques classify the remotely sensed image by using reflectance properties of ... more 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.
Classification techniques classify the remotely sensed image by using reflectance properties of p... more 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.
— Unsupervised classification creates clusters by grouping pixels based on the reflectance proper... more — Unsupervised classification creates clusters by grouping pixels based on the reflectance properties of pixels. This paper presents a new approach to classify multi-spectral remotely sensed image using pixels' density in N-dimensional scatterplot. It first finds the densely populated clusters in N-dimensional scatter plot and then finds gravity centers of these densely populated clusters. Later, multispectral image is classified using minimum distance to gravity classifier. At the beginning of classification, this approach neither makes extreme assumption of considering each pixel as a different cluster nor goes to the other extreme by considering all the pixels in a single cluster. It follows the middle path by making assumption of some pixels as gravity centers of different clusters before classifying the image. It creates clusters of equal size in N-dimensional scatter plot and picks up the densely populated clusters. All these clusters are recursively iterated for self-adjustment of gravity centers using mathematical algorithm. The approach uses gravitational force for merging two nearby clusters. Here, gravity center of a cluster is calculated by summing up the spectral bands' values and dividing it by number of pixels within that cluster. When two nearby clusters are merged, their gravity centers are also adjusted accordingly. This provides the most densely populated clusters and their gravity centers. Now, these gravity centers can be used to classify the remotely sensed image using minimum distance to gravity center classifier.
Papers by Adarsh K Khare
Image Classification Using Humps of Histogram
2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), 2016
Classification techniques classify the remotely sensed image by using reflectance properties of p... more 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.
Techniques for Remote Presence Subscription
Unsupervised Classification Using Frequency Density of Histogram
In classification, pixels with common characteristics are grouped together. User uses the pixels ... more In classification, pixels with common characteristics are grouped together. User uses the pixels density in N-dimensional Euclidian space to find out the frequency centers for classifying the image. The volume of N-dimensional Euclidian space is product of maximum grey level values. If maximum grey level value of spectral band is M, then volume of N-dimensional Euclidian space would be M ´ M ´ M … N times. The traditional approach finds the frequency centers from the product of maximum grey level values while this new approach finds the frequency centers from the sum of maximum grey level values. This approach draws histogram for all the spectral bands. While drawing histogram, many grey level values would be having zero frequencies. These zero frequency grey level values are not considered for finding frequency centers. Only non-zero frequency grey level values are considered for finding the frequency centers. So, this approach finds the frequency centers from pixels less than the ...
Unsupervised Classification Using Gravity Centers from Scatter Plot
2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), 2016
— Unsupervised classification creates clusters by grouping pixels based on the reflectance proper... more — Unsupervised classification creates clusters by grouping pixels based on the reflectance properties of pixels. This paper presents a new approach to classify multi-spectral remotely sensed image using pixels' density in N-dimensional scatterplot. It first finds the densely populated clusters in N-dimensional scatter plot and then finds gravity centers of these densely populated clusters. Later, multispectral image is classified using minimum distance to gravity classifier. At the beginning of classification, this approach neither makes extreme assumption of considering each pixel as a different cluster nor goes to the other extreme by considering all the pixels in a single cluster. It follows the middle path by making assumption of some pixels as gravity centers of different clusters before classifying the image. It creates clusters of equal size in N-dimensional scatter plot and picks up the densely populated clusters. All these clusters are recursively iterated for self-adjustment of gravity centers using mathematical algorithm. The approach uses gravitational force for merging two nearby clusters. Here, gravity center of a cluster is calculated by summing up the spectral bands' values and dividing it by number of pixels within that cluster. When two nearby clusters are merged, their gravity centers are also adjusted accordingly. This provides the most densely populated clusters and their gravity centers. Now, these gravity centers can be used to classify the remotely sensed image using minimum distance to gravity center classifier.
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Conference Presentations by Adarsh K Khare
Papers by Adarsh K Khare