Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to count animals. Previous studies have demonstrated the feasibility of using very high resolution multispectral satellite images for animal detection, but to date, the practicality of detecting animals from space using panchromatic imagery has not been proven. This study demonstrates that it is possible to detect and count large mammals (e.g., wildebeests and zebras) from a single, very high resolution GeoEye-1 panchromatic image in open savanna. A novel semi-supervised object-based method that combines a wavelet algorithm and a fuzzy neural network was developed. To discern large mammals from their surroundings and discriminate between animals and non-targets, we used the wavelet technique to highlight potential objects. To make full use of geometric attributes, we carefully trained the classifier, using the adaptive-network-based fuzzy inference system. Our proposed method (with an accuracy index of 0.79) significantly outperformed the traditional threshold-based method (with an accuracy index of 0.58) detecting large mammals in open savanna.

[1]  Josiane Zerubia,et al.  An automatic counter for aerial images of aggregations of large birds , 2011 .

[2]  Bingbing Ni,et al.  HCP: A Flexible CNN Framework for Multi-Label Image Classification , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Eugenia Minca,et al.  Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources , 2015 .

[4]  Jie Zhao,et al.  An Algorithm of Dim and Small Target Detection Based on Wavelet Transform and Image Fusion , 2012, 2012 Fifth International Symposium on Computational Intelligence and Design.

[5]  Zhang Yi,et al.  Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering , 2012, IEEE Transactions on Cybernetics.

[6]  Mohan M. Trivedi,et al.  A neural network filter to detect small targets in high clutter backgrounds , 1995, IEEE Trans. Neural Networks.

[7]  Jianguo Liu,et al.  Essential Image Processing and GIS for Remote Sensing , 2009 .

[8]  Hugh Griffiths,et al.  Wavelet detection scheme for small targets in sea clutter , 2002 .

[9]  C. A. Mücher,et al.  Environmental science: Agree on biodiversity metrics to track from space , 2015, Nature.

[10]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[11]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[12]  Andrea S. Laliberte,et al.  Automated wildlife counts from remotely sensed imagery , 2003 .

[13]  Jf Baldwin,et al.  An Introduction to Fuzzy Logic Applications in Intelligent Systems , 1992 .

[14]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[15]  Louis-Paul Rivest,et al.  Calving photocensus of the Rivière George Caribou Herd and comparison with an independent census , 1996 .

[16]  Anil K. Jain,et al.  Clustering Millions of Faces by Identity , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  C. Güler,et al.  Delineation of hydrochemical facies distribution in a regional groundwater system by means of fuzzy c‐means clustering , 2004 .

[18]  James R. Zeidler,et al.  Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data , 1993, IEEE Trans. Image Process..

[19]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..

[20]  Dianyuan Han,et al.  Comparison of Commonly Used Image Interpolation Methods , 2013 .

[21]  Maryam Zekri,et al.  Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System , 2012, Journal of medical signals and sensors.

[22]  R. Vidal,et al.  Sparse Subspace Clustering: Algorithm, Theory, and Applications. , 2013, IEEE transactions on pattern analysis and machine intelligence.

[23]  Andrew K. Skidmore,et al.  A wavelet‐based approach to evaluate the roles of structural and functional landscape heterogeneity in animal space use at multiple scales , 2015 .

[24]  Angelo Chianese,et al.  Small target detection using wavelets , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[25]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[26]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[27]  Huchuan Lu,et al.  Saliency Detection via Graph-Based Manifold Ranking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Elizabeth A. Masden,et al.  Ecological drivers of change at South Georgia: the krill surplus, or climate variability , 2012 .

[29]  David Pairman,et al.  Semi-automated penguin counting from digital aerial photographs , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[30]  Richard F. W. Barnes,et al.  The problem of precision and trend detection posed by small elephant populations in West Africa , 2002 .

[31]  Laurent Itti,et al.  Saliency and Gist Features for Target Detection in Satellite Images , 2011, IEEE Transactions on Image Processing.

[32]  Coskun Özkan.  Surface Interpolation by Adaptive Neuro-fuzzy Inference System Based Local Ordinary Kriging , 2006, ACCV.

[33]  Derya Avci,et al.  An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange , 2010, Expert Syst. Appl..

[34]  David Casasent,et al.  Detection filters and algorithm fusion for ATR , 1997, IEEE Trans. Image Process..

[35]  D. Doak,et al.  Abstracts, Reviews, and Meetings , 2011, Ecological Restoration.

[36]  George Pierce Jones,et al.  THE FEASIBILITY OF USING SMALL UNMANNED AERIAL VEHICLES FOR WILDLIFE RESEARCH , 2003 .

[37]  Brian Ng,et al.  The potential of 2D wavelet transforms for target detection in sea-clutter , 2015, 2015 IEEE Radar Conference (RadarCon).

[38]  Han Olff,et al.  Why are wildebeest the most abundant herbivore in the Serengeti , 2015 .

[39]  Bruce C. Lubow,et al.  A collaborative approach for estimating terrestrial wildlife abundance , 2012 .

[40]  Fei Wang,et al.  Multi-scale target detection in SAR image based on visual attention model , 2015, 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR).

[41]  C. Glasbey,et al.  Image analysis and three‐dimensional modelling of pores in soil aggregates , 1991 .

[42]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[43]  Adam T. Ford,et al.  Conservation Challenges Facing African Savanna Ecosystems , 2016 .

[44]  Mark D. Anderson,et al.  Using object-based analysis of image data to count birds: mapping of Lesser Flamingos at Kamfers Dam, Northern Cape, South Africa , 2011 .

[45]  A Viarengo,et al.  Development of an expert system for the integration of biomarker responses in mussels into an animal health index , 2007, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.

[46]  Zhou Quan,et al.  RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment , 2008, IEEE Transactions on Power Systems.

[47]  Jacob R. Goheen,et al.  Serengeti IV: Sustaining Biodiversity in a Coupled Human-Natural System , 2015 .

[48]  Yudong Zhang,et al.  Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM) , 2015, Entropy.

[49]  Qi Wang,et al.  Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[50]  Yoshua Bengio,et al.  No Unbiased Estimator of the Variance of K-Fold Cross-Validation , 2003, J. Mach. Learn. Res..

[51]  S. Stuart,et al.  Wildlife in a changing world : an analysis of the 2008 IUCN red list of threatened species , 2009 .

[52]  Gerald L. Kooyman,et al.  Correction: An Emperor Penguin Population Estimate: The First Global, Synoptic Survey of a Species from Space , 2012, PLoS ONE.

[53]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[54]  Li Fei-Fei,et al.  DenseCap: Fully Convolutional Localization Networks for Dense Captioning , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[55]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[56]  Debin Gao,et al.  Control Flow Obfuscation Using Neural Network to Fight Concolic Testing , 2014, SecureComm.

[57]  David S. Gilmer,et al.  Goose counts from aerial photographs using an optical digitizer , 1988 .

[58]  Steeve D. Côté,et al.  Aerial Surveys Vs Hunting Statistics To Monitor Deer Density: The Example Of Anticosti Island, Québec, Canada , 2007 .

[59]  Peter T. Fretwell,et al.  Whales from Space: Counting Southern Right Whales by Satellite , 2014, PloS one.

[60]  Xuelong Li,et al.  Saliency Detection by Multiple-Instance Learning , 2013, IEEE Transactions on Cybernetics.

[61]  C. Margules,et al.  Wombats detected from space , 1980 .

[62]  Geoff Groom,et al.  Remote sensing image data and automated analysis to describe marine bird distributions and abundances , 2013, Ecol. Informatics.

[63]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[64]  Zheng Yang,et al.  Spotting East African Mammals in Open Savannah from Space , 2014, PloS one.

[65]  Pasquale Maglione,et al.  Very High Resolution Optical Satellites: An Overview of the Most Commonly used , 2016 .

[66]  Ephraim.  Speech enhancement using a minimum mean square error short-time spectral amplitude estimator , 1984 .

[67]  Shuyuan Yang,et al.  Fusion of Panchromatic and Multispectral Images via Coupled Sparse Non-Negative Matrix Factorization , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[68]  G. Witmer.  Wildlife population monitoring: some practical considerations , 2005 .

[69]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[70]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[71]  Nicolas Lecomte,et al.  Polar Bears from Space: Assessing Satellite Imagery as a Tool to Track Arctic Wildlife , 2014, PloS one.

[72]  Iasonas Kokkinos,et al.  Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[73]  Le Wang,et al.  How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: a review , 2016 .

[74]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[75]  Bean Yin Lee,et al.  Application of the Discrete Wavelet Transform to the Monitoring of Tool Failure in End Milling Using the Spindle Motor Current , 1999 .

[76]  Senén Barro,et al.  Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..

[77]  Andrew K. Skidmore,et al.  Improved understorey bamboo cover mapping using a novel hybrid neural network and expert system , 2009 .

[78]  S. Harbo,et al.  Estimating moose population parameters from aerial surveys , 1986 .

[79]  Sarat Kumar Patra,et al.  Transmission rate prediction for Cognitive Radio using Adaptive Neural Fuzzy Inference System , 2010, 2010 5th International Conference on Industrial and Information Systems.

[80]  J. Hopcraft,et al.  Serengeti wildebeest migratory patterns modeled from rainfall and new vegetation growth. , 2006, Ecology.

[81]  Sungho Kim.  High-Speed Incoming Infrared Target Detection by Fusion of Spatial and Temporal Detectors , 2015, Sensors.

[82]  Douglas J. King,et al.  Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration , 2002 .

[83]  Gerald L. Kooyman,et al.  An Emperor Penguin Population Estimate: The First Global, Synoptic Survey of a Species from Space , 2012, PloS one.

[84]  D. Adam.  The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance , 2004 .

[85]  J. Buckley,et al.  Fuzzy neural networks: a survey , 1994 .

[86]  J. Kingsbury.  The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance , 2004 .

[87]  Md. Nasir Sulaiman,et al.  An effective fuzzy C-mean and type-2 fuzzy logic for weather forecasting. , 2009 .

[88]  Hideyuki Takagi,et al.  Neural networks designed on approximate reasoning architecture and their applications , 1992, IEEE Trans. Neural Networks.

[89]  Xiangzhi Bai,et al.  Survey on Dim Small Target Detection in Clutter Background: Wavelet, Inter-Frame and Filter Based Algorithms , 2011 .

[90]  Ritu Tiwari,et al.  Fuzzy Neuro Systems for Machine Learning for Large Data Sets , 2009, 2009 IEEE International Advance Computing Conference.

[91]  John F. Piatt,et al.  COMPUTER-AIDED PROCEDURE FOR COUNTING WATERFOWL ON AERIAL PHOTOGRAPHS , 1990 .

[92]  René Vidal,et al.  Sparse Subspace Clustering: Algorithm, Theory, and Applications , 2012, IEEE transactions on pattern analysis and machine intelligence.

[93]  Hui Wu,et al.  Typical Target Detection in Satellite Images Based on Convolutional Neural Networks , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[94]  Yoshua Bengio,et al.  Scaling learning algorithms towards AI , 2007 .

[95]  C. P. Kurian,et al.  ANFIS Model for the Time Series Prediction of Interior Daylight Illuminance , 2006 .

[96]  W. H. Anderson,et al.  An image-processing program for automated counting , 1996 .

[97]  강 현배,et al.  웨이블릿 이론과 응용 = Wavelet theory and Its applications , 2001 .