Image fusion using sparse overcomplete feature dictionaries

1. A computer-implemented method, comprising:

learning, by a computing system, a sparse overcomplete feature dictionary for classifying and/or clustering a remote sensing image dataset;

building, by the computing system, a local sparse representation of the image dataset using the learned sparse overcomplete feature dictionary; and

applying, by the computing, system, a local maximum pooling operation on the local sparse representation to produce a translation-tolerant representation of the image dataset.

2. The computer-implemented method of claim 1 , wherein the learning of the sparse overcomplete feature dictionary comprises:

initializing, by the computing system, atoms φ k of a feature dictionary Φ either by imprinting a set of unlabeled patches x, or by initializing φ k using random vectors;

for each unlabeled patch in x, seeking, by the computing system, a coefficient vector y such that y is sparse and Φy approximates x;

finding, by the computing system, an approximate solution for y; and

updating Φ, by the computing system, using a learning rule.

3. The computer-implemented method of claim 1 , wherein the building of the local sparse representation of the image dataset comprises applying a greedy matching pursuit algorithm or a non-convex basis pursuit algorithm.

4. The computer-implemented method of claim 1 , further comprising:

down-sampling the image dataset, by the computing system, prior to building the local sparse representation of the image dataset.

5. The computer-implemented method of claim 1 , further comprising:

classifying and/or clustering, by the computing system, an object within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

6. The computer-implemented method of claim 5 , wherein the classifying and/or clustering of the object comprises categorizing the translation-tolerant representation of the image dataset in a predetermined number of categories.

7. The computer-implemented method of claim 6 , wherein the categorizing of the translation-tolerant representation of the image dataset comprises applying a cluster model to the translation-tolerant representation of the image dataset.

8. The computer-implemented method of claim 6 , further comprising:

augmenting, by the computing system, a predetermined number of background categories with a number of target categories using the sparse overcomplete feature dictionary and supervised clustering.

9. The computer-implemented method of claim 8 , further comprising:

classifying and/or clustering the image dataset, by the computing system, in response to a minimum distance algorithm applied across the augmented categories.

10. A computer program embodied on a non-transitory computer-readable medium, the program configured to cause at least one processor to:

initialize atoms φ k of a feature dictionary Φ by imprinting a set of unlabeled patches x;

for each unlabeled patch in x, seek a coefficient vector y such that y is sparse and Φy approximates x;

find an approximate solution for y; and

update Φ using a learning rule.

11. The computer program of claim 10 , wherein the program is further configured to cause the at least one processor to find the approximate solution of y using a simple matching pursuit algorithm, orthogonal matching pursuits, an l−1 convex basis pursuit, or a non-convex basis pursuit.

12. The computer program of claim 10 , wherein the program is further configured to cause the at least one processor to draw the set of unlabeled patches x for an image dataset.

13. The computer program of claim 12 , wherein the learning rule is defined by:

∀φ k ∈Φ,Δφ k =ηy k ( x−Φy )

where η is a learning rate and k is a number of indexes over φ k in Φ.

14. An apparatus, comprising:

memory storing computer program instructions; and

at least one processor configured to execute the computer program instructions, the at least one processor configured to:

learn a sparse overcomplete feature dictionary for an image dataset,

build a local sparse representation of the image dataset using the learned feature dictionary,

apply a local maximum pooling operation on the local sparse representation to produce a translation-tolerant representation of the image dataset, and

classify and/or cluster an object within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

15. The apparatus of claim 14 , wherein the building of the local sparse representation of the image dataset comprises applying a greedy matching pursuit algorithm or a non-convex basis pursuit noisy algorithm.

16. The apparatus of claim 14 , wherein the at least one processor is further configured to down-sample the image dataset prior to building the local sparse representation of the image dataset.

17. The apparatus of claim 14 , wherein the detecting of the object comprises categorizing the translation-tolerant representation of the image dataset in a predetermined number of categories.

18. The apparatus of claim 17 , wherein the at least one processor is further configured to:

learn a number of categories of objects in the image dataset by using supervised and unsupervised clustering of sparse image region representations in the sparse overcomplete feature dictionary; and

classify the image dataset in response to a minimum distance algorithm applied across the learned object categories.