Automated Hyperspectral Target Detection and Change Detection from an Airborne Platform: Progress and Challenges

Transcription

1 Automated Hyperspectral Target Detection and Change Detection from an Airborne Platform: Progress and Challenges July 2010 Michael Eismann, AFRL Joseph Meola, AFRL Alan Stocker, SCC

2 Presentation Outline Contet Evolving Target Detection Applications CAP ARCHER System Eperience Signature-Matched Target Detection Detector Formulation Atmospheric Normalization High Resolution Imagery False Alarm Reduction Change Detection Promise and Problems 2

3 Evolving Applications Hyperspectral system capabilities for automated, real-time target detection are maturing Significant focus has been military vehicle detection Several airborne sensor systems have been demonstrated Evolving applications are more demanding Search and rescue: diverse, perhaps non-distinct targets Asymmetric warfare: diverse, smaller, fleeting targets Counter-terrorism: indistinguishable targets Urban warfare: comple background clutter What must we do to either become or stay relevant? Provide useful performance under realistic scenarios 3

4 Eample: CAP ARCHER Production program for 15 HSI sensor/processing systems for CAP missions Search and Rescue (SAR) Counter Drug (CD) Disaster Relief (DR) Homeland Security (HLS) NovaSol VNIR HSI DALSA Pirahna-2 VIS Imager CMIGITS III GPS/IMU U.S. AIR FORCE AUXILIARY Airborne Real-Time Cueing Hyperspectral Enhanced Reconnaissance On-board real-time processor 21 flat panel display 4

5 ARCHER Processing On-Board, Real-Time Processing Relative Calibration SSRX Anomaly Detection Spectral Matched Filter (SMF) Geo-Rectified Display Cued Image Chip Display Post-Flight, Ground Processing Replica of On-Board Processing Spectral Change Detection Sub-Piel Registration Chronochrome Covariance Equalization 5

6 F-15 Search Deployment Main wreckage found by anomaly detection over 25 km 2 area Anomaly detector threshold set at P fa = In-scene fuselage spectrum used to find other aircraft parts Smaller parts found using SMF with CE-normalized spectrum 246 false alarms in search area dismissed by manual evaluation of high resolution image chips 6

7 Main Wreckage Site 7

8 Detected Aircraft Parts 8

9 Cue rate (min -1 ) False Alarm Problem F-15 search was performed post-flight with significant manual intervention (analysis of high resolution image chips) Practical application of real-time, on-board capabilities requires further reductions in the false alarm rate Evolving applications are making this situation even worse SMF SSRX 1000 km 2 /hr 200 km 2 /hr Practical Range False Alarm Rate (km -2 ) 9

10 General Observations Anomaly detectors produce far too many false alarms for most practical, real-time applications Signature-based detection methods require better selectivity to reduce false alarms from man-made objects Signature-based detectors need more robust atmospheric normalization methods for signature matching Automated, spatial-based methods are needed to reduce false alarms in the stream of image chips Change detection is a promising method for dealing with comple backgrounds for many applications 10

11 11 Signature-Based Detection Spectral Matched Filter Currently employed in ARCHER PDF under H 1 resembles noise (background) and not target Joint Subspace Detector More appropriate signal model Sub-piel variations have also been derived n s n a H H : : 1 0 b b b b b b m s C m s m C m s ˆ ˆ ˆ ˆ ˆ ˆ 1 1 r SMF n s n b : : 1 0 H H b n n n b b n n n b m C B B C B B C m m C S S C S S C m ˆ ˆ ˆ ˆ T T T T T T r JSD

12 Conceptual Comparison DECISION THRESHOLD DECISION THRESHOLD TARGET SUBSPACE DECISION THRESHOLD TARGET TARGET TARGET ANOMALY ANOMALY ANOMALY BACKGROUND ANOMALY DETECTOR (SSRX) BACKGROUND MATCHED FILTER (MF) BACKGROUND SUBSPACE JOINT SUBSPACE DETECTOR (JSD) Better selectivity arises due to quadratic decision surface Other quadratic and nonlinear methods aim to do the same Fundamentally demands accurate known of epected target variance Target variance is often due to atmospheric normalization uncertainties Problem: how does one best determine the target subspace T 12

13 Atmospheric Normalization Linear Radiometric Model g t a d b i ρ g p o n Aggregate gain Aggregate offset r = object reflectance signature = measured spectrum g = sensor gain o = sensor offset n = sensor noise t = atmospheric transmission p = atmospheric path radiance d= direct solar illumination i = indirect downwelling illumination a = direct shadow coefficient b = indirect shadow coefficient Requires absolute sensor calibration Changes with environmental conditions Dependent on the local environment 13

14 Normalization Alternatives Empirical Methods Vegetation Normalization ARCHER Finds aggregate bias from dark measurements Finds aggregate gain from vegetation measurements Quick Atmospheric Compensation (QUAC) Finds aggregate gain based on diverse set of spectra Model-Based Methods FLAASH Map radiance to reflectance by finding best fit to MODTRAN parameters Invariant Subspace Detection Determine target subspace by forward modeling over all possible atmospheric conditions 14

15 Vegetation Normalization Empirical methods are intriguing from a practical perspective Ecellent absolute calibration is very difficult to achieve Algorithms are computationally simple and work fairly well Typical Result General Observations Good match to primary spectral features that drive detection Mismatch in absolute reflectance levels and coarse spectral shape often occurs Etensions Under Consideration Incorporate estimation uncertainties in target subspace Use vegetation library instead of a single reference spectrum Incorporate local illumination variations 15

16 AutoMatch Algorithm (Smetek) Subspace detector that uses target and vegetation library and linear radiometric model to derive the target subspace Library Signatures Derived Target Ensemble Target Target Ensemble Vegetation Actual Targets Etension to incorporate local illumination variations requires separate estimates of global direct and diffuse components Semi-empirical methodology seems plausible 16

17 False Alarm Mitigation (GeoID) Multi-Hypothesis Target Identification Evaluate all constrained target models with background endmember model Rank signature models relative to null-hypothesis model Background-Only Model Target Observation Background-Only Target Model: Correct Signature Target Observation Signature + Background Signature Component Background Component Residual Error Residual Error Target Model: Wrong Signature Target Observation Signature + Background Signature Component Background Component Library signature ranking metric Error ratio = Background-Only Error Target + Background Error Model c 2 Fit Error Ratio, f Fill Fraction Background Only Bkg. + Wrong Sig % Bkg. + Correct Sig % Residual Error Correct signature 12.1 times better fitting error than background alone. 17

18 High Resolution Image Fusion Automated spatial matching based on the high resolution images can aid in false alarm mitigation Information in the HRI is currently under-utilized Approaches being considered: Cueing: apply spatial matching to stream of image chips Unique target: spatial template derived from edge detection filter General man-made target: match to simple geometric shapes/distances Feature Fusion: combine spatial features with spectral data in a matched detector Eample: Two-dimensional PCA features from HRI data Image Fusion: perform HSI resolution enhancement based on HRI and perform spatial-spectral detection Computationally comple and not likely to improve performance 18

19 Spatially-Enhanced Invariant Recognition (Healey) ARCHER Units HSI Data Spectral Detector Processing Chain spectral Target Model spectral cues spatial Spatial Verification Enhanced Detection Results Airplane Spectral and Spatial Model ARCHER Image 1 (656m) HRI Data Wavelength [microns] Search Area, ARCHER Image 2 (722m) HRI Data for Top Spectral Detects with Spatial Match Highlighted 19

20 Change Detection Motivation Cultural clutter is a challenge for HSI sensors High false alarm rates due to large spectral diversity of clutter Change processing has potential for routine imaging CONOPS Eploits time dimension May require sophisticated normalization methods 20

21 ARCHER Change Detection Assumes global affine relationship between reference and test spectra after fine spatial registration Test Spectrum () + - Detector Detected Changes Reference Spectrum (y) Predictor Change Residual (d) Prediction ( ˆ ) ˆ Tˆ y ˆ y d y Chronochrome (Wiener Filter) dˆ y ˆ 1 y CyCy T m Tˆ y m y Covariance Equalization dˆ Tˆ y y C m 1/ 2 C Tˆ 1/ 2 y y m y 21

22 Global Change Detection Eample: ARCHER imagery collected at Fort A.P. Hill Chronochrome Covariance Equalization 22

23 Class-Conditional Change Detection Estimates affine parameter on a class-conditional basis after performing SEM clustering on the joint spectral data Can achieve similar results using a local predictor (Kwan) Chronochrome Covariance Equalization 23

24 Detection Performance Comparison Global Class-Conditional 1 FA/km 2 24

25 Signature-Based Change Detection High Resolution Image SEM Segmentation SMF Filter Output SMF Filter Output w/ Class-Conditional CC 25

26 Model-Based Change Detection Full data model for piel m (1) (2) [ m] t [ m] t (1) (2) (1) (1) (1) (1) (1) (1) a [ m] d b [ m] i ρ[ m] p n [ m] (2) (2) (2) (2) (2) (2) a [ m] d b [ m] i ρ[ m] p n [ m] Subspace data model for piel m (1) (2) [ m] [ m] (1) (1) (1) (1) (1) (1) a [ m] Dε b [ m] Iε ρ[ m] Pε n [ m] (2) (2) (2) (2) (2) (2) a [ m] Dε b [ m] Iε ρ[ m] Pε n [ m] Unknown parameter vector for cube θ a (2) (1) T (2) ε, ε [1],, a (2) T, a (1) [ M ], b Cost Function f θ M m1 (1) [1],, a (2) (1) [1],, b 2 [ M ], b (2) (1) [ M ], [1],, b (1) [ M ], T T ρ[1],, ρ[ M ] (1) (2) (2) [ m] ˆ [ m θ] [ m] ˆ [ m θ] f θ 2 M m1 m 26

27 Proof of Concept Results Synthetic spectral data created using MODTRAN Solar position and shadow conditions varied between time-1 and time-2 data M=100 piels simulated with a single piel change target (m=20) Simulated reflectance change Residual optimization error 27

28 Conclusion Advances in hyperspectral algorithm technology are needed for practical, real-time detection of small targets False alarm rates with standard algorithms are still too high Target detection scenarios are getting more difficult Absolute sensor calibration cannot be epected Need to make better use of potentially available information Epected target signature statistics Semi-empirical atmosphere and illumination models High resolution imagery Target dynamics 28