Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry
Abstract
:1. Introduction
2. Hyperspectral Sensors
3. Operations Prior to Flight and Post-Acquisition Data Pre-Processing
4. Data Processing and Analysis
4.1. Dimension Reduction
4.2. Target and Anomaly Detection
- geometrically: uses a subspace of the spectra in which the matrix defining the variability of the subspace can be a spectra signatures library (on data or even vectors), obtained from statistical techniques;
- statistically: spectral variability is described accordingly with probability distribution models. Calculus such as mean vector and covariance matrix are applied in different moments under a multivariate normal distribution assumption. Thus, variability can be measured from a uniform distribution of the data space.
- Probability Density Models: this kind of models consist in a scatter set of the reflectance values for a range of spectral bands that aims to identify different spectral classes in a scene using clustering algorithms (e.g., k-means) and classification methods (e.g., color attribution);
- Subspace models: are applied to analyze the variability within an M-dimensional band space, from a K-dimensional set, where M < K. PCA is one of the approaches within this probabilistic model;
- Linear spectral mixing models: are used to estimate the composition of the image’s pixels in those cases wherein there are pixels composed of a small number of unique spectral signatures corresponding to different components (endmembers).
4.2.1. Full Pixel Detection
4.2.2. Subpixel Target Detection
4.3. Other Classification Methods
4.4. Vegetation Indices
5. Software and Libraries for Dealing with Hyperspectral Data
6. Applications in Agriculture and Forestry Areas
6.1. Agriculture
6.2. Forestry
6.3. Agroforestry
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
ACD | Anomalous Change Detection |
AIS | Airborne Imaging Spectrometer |
AMF | Adaptive Matched Filters |
API | Application Programming Interface |
AVIRIS | Airborne Visible/Infrared Imaging Spectrometer |
BBA | Bundle Block Adjustment |
BIL | Band-Interleaved-by-Line |
BIP | Band-Interleaved-by-Pixel |
BSQ | Band Sequential |
CARI | Chlorophyll Absorption Ratio Index |
CCD | Charge-Coupled Device |
CF | Continuum Fusion |
CFAR | Constant False Alarm Rate |
CMOS | Complementary Metal-Oxide-Semiconductor |
CNN | Convolutional Neural Networks |
DAFE | Decision Boundary Feature Matrix |
DBFE | Decision Boundary Feature Extraction |
DL | Deep Learning |
DN | Digital Numbers |
DSM | Digital Surface Model |
ENVI | ENvironment for Visualizing Images |
ERDAS | Earth Resource Data Analysis System |
ESWIR | Early Short-Wave InfraRed |
FAM | False Alarm Mitigation |
FLD | Fraunhofer Line Depth |
FNIR | Far Near InfraRed |
FPI | Fabry-Perot Interferometer |
FSWIR | Far Short-Wave InfraRed |
FWHM | Full Width At Half Maximum |
GaAs | Gallium Arsenide |
GCP | Ground Control Point |
GI | Greenness Index |
GI | Greenness Index |
GLR | Generalized Likelihood Ratio |
GLRT | Generalized Likelihood Ratio Test |
GNSS | Global Navigation Satellite Systems |
GPL | General Public License |
GPS | Global Positioning System |
GVI | Greenness Vegetation Index |
HIAT | Hyperspectral Image Analysis Toolbox |
HSI | Hyperspectral Imaging |
H-SSC | Hydrological Soil Surface Characteristics |
HypPy | Hyperspectral Python |
ICA | Independent Component Analysis |
IMU | Inertial Measurement Unit |
InAs | Indium Arsenide |
InGaAs | Indium Gallium Arsenide |
INS | Inertial Navigation Systems |
k-NN | k-Nearest Neighbor |
LIDAR | LIght Detection And Ranging |
LMM | Linear Mixed Models |
LR | Likelihood Ratio |
MCARI | Modified Chlorophyll Absorption Ratio Index |
MCR | Multivariate Curve Resolution |
MCT, HgCdTe | Mercury Cadmium Tellurium |
mNDVI | Modified Normalized Difference Vegetation Index |
MNF | Maximum Noise Fraction |
MSAVI | Modified Soil-Adjusted Vegetation Index |
MVSR | Modified Vegetation Stress Ratio |
NASA/JPL | National Aeronautics and Space Administration Jet Propulsion Laboratory |
NDVI | Normalized Difference Vegetation Index |
NIR | Near InfraRed |
NPCI | Normalized Pigment Chlorophyll Index |
OSAVI | Optimized Soil-Adjusted Vegetation Index |
PCA | Principal Component Analysis |
Pd | Probability of detection |
Pfa | Probability of false alarm |
PRI | Photochemical Reflectance Index |
RADAR | RAdio Detection/Direction And Ranging |
RBF | Radial Basis Function |
RDI | Regulated Deficit Irrigation |
RE | Red-Edge |
RGB | Red Green Blue |
ROC | Receiving Operating Characteristic |
RTT | Radiative Transfer Theory |
RX | Reed-Xiaoli |
SAM | Spectral Angle Mapper |
Si | Silicon |
SMARTS | Simple Model Of The Atmospheric Radiative Transfer Of Sunshine |
SNR | Signal-To-Noise Ratio |
SPy | Spectral Python |
SR | Simple Ratio |
SRPI | Simple Ratio Pigment Index |
SVDD | Support Vector Data Description |
SVM | Support Vector Machines |
TCARI | Transformed Chlorophyll Absorption Ratio Index |
THOR | Tactical Hyperspectral Operations Resource |
THz | submillimeter radiation |
TID | Target IDentification |
TSVM | Transductive Support Vector Machines |
TVI | Triangular Vegetation Index |
UAS | Unmanned Aircraft Systems |
UASI | Unmanned Aerial System Innovations |
UAV | Unmanned Aerial Vehicle |
UV-Vis | Ultraviolet-Visible |
VI | Vegetation Index |
VNIR | Visible and Near-Infrared |
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Spectral Information | Spatial Information | |
---|---|---|
Hyperspectral Imaging | ••• | ••• |
Multispectral Imaging | •• | ••• |
Spectroscopy | ••• | • |
RGB Imagery | • | ••• |
Manuf. | Sensor | Spectral Range (nm) | No. Bands | Spectral Resol. (nm) | Spatial Resol. (px) | Acquis. Mode | Weight (g) |
---|---|---|---|---|---|---|---|
BaySpec | OCI-UAV-1000 | 600–1000 | 100 | <5 b | 2048 d | P | 272 |
Brandywine Photonics | CHAI S-640 | 825–2125 | 260 | 5 c | 640 × 512 | P | 5000 |
CHAI V-640 | 350–1080 | 256 | 2.5 c | 640 × 512 | P | 480 | |
5 c | |||||||
10 c | |||||||
Cubert GmbH | S 185—FIREFLEYE SE | 450–950 | 125 | 4 c | 50 × 50 | S | 490 |
S 485—FIREFLEYE XL | 355–750 | 125 | 4.5 c | 70 × 70 | S | 1200 | |
450–950 | |||||||
550–1000 | |||||||
Q 285—FIREFLEYE QE | 450–950 | 125 | 4 c | 50 × 50 | S | 3000 | |
Headwall Photonics Inc., Fitchburg, MA, USA | Nano HyperSpec | 400–1000 | 270 | 6 b | 640 d | P | 1200 e |
Micro Hyperspec VNIR | 380–1000 | 775 837 923 | 2.5 b | 1004 d 1600 d | P | ≤3900 | |
HySpex | VNIR-1024 | 400–1000 | 108 | 5.4 c | 1024 d | P | 4000 |
Mjolnir V-1240 | 400–1000 | 200 | 3 c | 1240 d | P | 4200 | |
HySpex SWIR-384 | 1000–2500 | 288 | 5.45 c | 384 d | P | 5700 | |
MosaicMill | Rikola | 500–900 | 50 a | 10 b | 1010 × 1010 | S | 720 |
NovaSol | vis-NIR microHSI | 400–800 400–1000 380–880 | 120 180 150 | 3.3 c | 680 d | P | <450 |
Alpha-vis micro HSI | 400–800 350–1000 | 40 60 | 10 c | 1280 d | P | <2100 | |
SWIR 640 microHSI | 850–1700 600–1700 | 170 200 | 5 c | 640 d | P | 3500 | |
Alpha-SWIR microHSI | 900–1700 | 160 | 5 c | 640 d | P | 1200 | |
Extra-SWIR microHSI | 964–2500 | 256 | 6 c | 320 d | P | 2600 | |
PhotonFocus | MV1-D2048x1088-HS05-96-G2 | 470–900 | 150 | 10-12 b | 2048 × 1088 | P | 265 |
Quest Innovations | Hyperea 660 C1 | 400–1000 | 660 | - | 1024 d | P | 1440 |
Resonon | Pika L | 400–1000 | 281 | 2.1 c | 900 d | P | 600 |
Pika XC2 | 400–1000 | 447 | 1.3 c | 1600 d | P | 2200 | |
Pika NIR | 900–1700 | 164 | 4.9 c | 320 d | P | 2700 | |
Pika NUV | 350–800 | 196 | 2.3 c | 1600 d | P | 2100 | |
SENOP | VIS-VNIR Snapshot | 400–900 | 380 | 10 b | 1010 × 1010 | S | 720 |
SPECIM | SPECIM FX10 | 400–1000 | 224 | 5.5 b | 1024 d | P | 1260 |
SPECIM FX17 | 900–1700 | 224 | 8 b | 640 d | P | 1700 | |
Surface Optics Corp., San Diego, CA, USA | SOC710-GX | 400–1000 | 120 | 4.2 c | 640 d | P | 1250 |
XIMEA | MQ022HG-IM-LS100-NIR | 600–975 | 100+ | 4 c | 2048 × 8 | P | 32 |
MQ022HG-IM-LS150-VISNIR | 470–900 | 150+ | 3 c | 2048 × 5 | P | 300 |
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Adão, T.; Hruška, J.; Pádua, L.; Bessa, J.; Peres, E.; Morais, R.; Sousa, J.J. Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens. 2017, 9, 1110. https://doi.org/10.3390/rs9111110
Adão T, Hruška J, Pádua L, Bessa J, Peres E, Morais R, Sousa JJ. Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sensing. 2017; 9(11):1110. https://doi.org/10.3390/rs9111110
Chicago/Turabian StyleAdão, Telmo, Jonáš Hruška, Luís Pádua, José Bessa, Emanuel Peres, Raul Morais, and Joaquim João Sousa. 2017. "Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry" Remote Sensing 9, no. 11: 1110. https://doi.org/10.3390/rs9111110