Image processing system including iteratively biased training model probability distribution function and related methods
An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. Each training geospatial image may include pixels. The processor may be configured to operate a training model to identify a given feature from each of the training geospatial images, and to iteratively generate a probability distribution function based upon a number of pixels corresponding to the given feature and also based upon a bias factor being reduced with each iteration.
1. An image processing system comprising:
a processor and an associated memory configured to
store training data comprising a plurality of training geospatial images, each training geospatial image comprising a plurality of pixels, and
operate a training model to identify a given feature from each of the plurality of training geospatial images, and to iteratively generate a probability distribution function based upon a number of pixels corresponding to the given feature and also based upon a bias factor being reduced with each iteration.
2. The image processing system of claim 1 wherein the training model comprises a mask-based training model.
3. The image processing system of claim 1 wherein said processor is configured to reduce the bias factor to a terminal non-zero bias factor.
4. The image processing system of claim 1 wherein said processor is configured to reduce the bias factor to a terminal zero bias factor.
5. The image processing system of claim 1 wherein said processor is configured to use an initial bias factor so that the training model identifies a pixel corresponding to the given feature for each predetermined number of pixels that do not correspond to the given feature.
6. The image processing system of claim 1 wherein said processor is configured to generate the probability distribution function based upon an entropy of each training geospatial image.
7. The image processing system of claim 1 wherein said processor is configured to generate the probability distribution function based upon a Shannon entropy of each training geospatial image.
8. The image processing system of claim 1 wherein said processor is configured to operate the training model to identify the given feature from a plurality of geospatial images.
9. A method of processing an image comprising:
using a processor and an associated memory to
store training data comprising a plurality of training geospatial images, each training geospatial image comprising a plurality of pixels, and
operate a training model to identify a given feature from each of the plurality of training geospatial images, and to iteratively generate a probability distribution function based upon a number of pixels corresponding to the given feature and also based upon a bias factor being reduced with each iteration.
10. The method of claim 9 wherein the training model comprises a mask-based training model.
11. The method of claim 9 wherein using the processor comprises using the processor to reduce the bias factor to a terminal non-zero bias factor.
12. The method of claim 9 wherein using the processor comprises using the processor to reduce the bias factor to a terminal zero bias factor.
13. The method of claim 9 wherein using the processor comprises using the processor to use an initial bias factor so that the training model identifies a pixel corresponding to the given feature for each predetermined number of pixels that do not correspond to the given feature.
14. The method of claim 9 wherein using the processor comprises using the processor to generate the probability distribution function based upon an entropy of each training geospatial image.
15. The method of claim 9 wherein using the processor comprises using the processor to generate the probability distribution function based upon a Shannon entropy of each training geospatial image.
16. The method of claim 9 wherein using the processor comprises using the processor to operate the training model to identify the given feature from a plurality of geospatial images.
17. A non-transitory computer readable medium for processing an image, the non-transitory computer readable medium comprising computer executable instructions that when executed by a processor cause the processor to perform operations comprising:
storing training data comprising a plurality of training geospatial images, each training geospatial image comprising a plurality of pixels; and
operating a training model to identify a given feature from each of the plurality of training geospatial images, and to iteratively generate a probability distribution function based upon a number of pixels corresponding to the given feature and also based upon a bias factor being reduced with each iteration.
18. The non-transitory computer readable medium of claim 17 wherein the training model comprises a mask-based training model.
19. The non-transitory computer readable medium of claim 17 wherein the operations comprise reducing the bias factor to a terminal non-zero bias factor.
20. The non-transitory computer readable medium of claim 17 wherein the operations comprise reducing the bias factor to a terminal zero bias factor.
21. The non-transitory computer readable medium of claim 17 wherein the operations comprise using an initial bias factor so that the training model identifies a pixel corresponding to the given feature for each predetermined number of pixels that do not correspond to the given feature.
22. The non-transitory computer readable medium of claim 17 wherein the operations comprise generating the probability distribution function based upon an entropy of each training geospatial image.
23. The non-transitory computer readable medium of claim 17 wherein the operations comprise generating the probability distribution function based upon a Shannon entropy of each training geospatial image.
24. The non-transitory computer readable medium of claim 17 wherein the operations comprise operating the training model to identify the given feature from a plurality of geospatial images.