/Hyperspectral-image-target-detection-based-on-sparse-representation

Hyperspectral image Target Detection based on Sparse Representation

Primary LanguageMATLABMIT LicenseMIT

Introduction

Hyperspectral image target detection based on sparse representation,an effective method in Pattern Recognition. Target detection aims to separate the specific target pixel from the various backgrounds by the use of known target pixels or anomalous properties. The proposed approach relies on the binary hypothesis model of an unknown sample induced by sparse representation. The sample can be sparsely represented by training samples from the background and target dictionary. The sparse vector in the model can be recovered by a greedy algorithm OMP .

Author

Bibliography

  1. Sub-space Matching

    Chen Y, Nasrabadi N M, Tran T D. Hyperspectral image classification using dictionary-based sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3973-3985.

  2. Dual Window

    Chen Y, Nasrabadi N M, Tran T D. Sparse representation for target detection in hyperspectral imagery[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3): 629-640.

Theory (SMSD)

The problem of target detection can be regarded as a competitive relationship of two hypotheses (Background) and (Target).

T and B are both matrices, their column vectors are divided into target and background subspace. and form the coefficient vectors of the coefficients, respectively. N denotes Gaussian random noise, [T,B]represents a cascade matrix of T and B.

Suppose ,When D is greater than a certain threshold η, then X is the target.

That means we need to find a projection matrix p.

By the sparse representation of knowledge, it is known that the residual error of signal reconstruction can be expressed as:

After comparison we can find:

Suppose ,When D is greater than a certain threshold η, then X is the target.

Then it is based on the ROC curve to compare different threshold effects, resulting in the final result.

However, it is better to amend Denominator as in practice.

Data

San Diego hyperspectral dataset (400*400)

SOURCE

GroundTruth (100*100)

DT

File

  • ./Dict_build.m dictionary build with part of target index
  • ./Dict_build_all.m dictionary build with all of the target index
  • detect.m Basic algorithm to detect target
  • ./local/local_suitable.m Dual_window method with smooth and suitable to adjust and plot
  • ./local/local_smooth.m Dual_window method with smooth
  • ./local/local_smooth.m Dual_window method with smooth and plot results

Rebuilt

Sparse coefficients

SC

Rebuild by dict_b and dict_t

SC_REBUILD

Detection

Sparse Representation

SR_good SR_bad

Dual window

DW

Dual window with smooth

DWS

SVM

SVM

Positive Negative Total Accuracy
Train 37 6629 6666
Test 57 9943 10000 0.9989

Fisher

1. Using all of data

ALL

Positive Negative Total Accuracy
Train 57 9943 10000
Test 57 9943 10000 0.9926 
2. Using part of the data

PART

Positive Negative Total Accuracy
Train 37 6629 6666
Test 57 9943 10000 0.985

ROC curve

ROC ROC_ALL

Platform

How to run

For Sparse Representation

mat/detect.m

mat/local/local_smooth.m

mat/local/local_smooth_plot.m

mat/local/local_suitable.m

For SVM

svm/main.cpp

For Fisher_part

fisher/fisher.cpp

For Fisher_all

fisher/fisher_all.cpp

Contact Us

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