Ground object detection in worldview images

Ground object detection is important for many civilian applications. Counting the number of cars in parking lots can provide very useful information to shop owners. Tent detection and counting can help humanitarian agencies to assess and plan logistics to help refugees. In this paper, we present some preliminary results on ground object detection using high resolution Worldview images. Our approach is a simple and semi-automated approach. A user first needs to manually select some object signatures from a given image and builds a signature library. Then we use spectral angle mapper (SAM) to automatically search for objects. Finally, all the objects are counted for statistical data collection. We have applied our approach to tent detection for a refugee camp near the Syrian-Jordan border. Both multispectral Worldview images with eight bands at 2 m resolution and pansharpened images with four bands at 0.5 m resolution were used. Moreover, synthetic hyperspectral (HS) images derived from the above multispectral (MS) images were also used for object detection. Receiver operating characteristics (ROC) curves as well as detection maps were used in all of our studies.

[1]  Yuzhong Shen,et al.  Deep learning for effective detection of excavated soil related to illegal tunnel activities , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[2]  Chiman Kwan,et al.  A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Chiman Kwan,et al.  Bum scar detection using cloudy MODIS images via low-rank and sparsity-based models , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[4]  Chiman Kwan,et al.  Improved target detection for hyperspectral images using hybrid in-scene calibration , 2017 .

[5]  Chiman Kwan,et al.  On the use of radiance domain for burn scar detection under varying atmospheric illumination conditions and viewing geometry , 2017, Signal Image Video Process..

[6]  Chiman Kwan,et al.  A joint sparsity approach to tunnel activity monitoring using high resolution satellite images , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[7]  Yuzhong Shen,et al.  Combining Satellite Images with Feature Indices for Improved Change Detection , 2018, 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[8]  Chiman Kwan,et al.  Hyperspectral image super-resolution: a hybrid color mapping approach , 2016 .

[9]  D. Nguyen,et al.  A comparative study of several supervised target detection algorithms for hyperspectral images , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[10]  Chiman Kwan,et al.  Blind Quality Assessment of Fused WorldView-3 Images by Using the Combinations of Pansharpening and Hypersharpening Paradigms , 2017, IEEE Geoscience and Remote Sensing Letters.

[11]  Chiman Kwan,et al.  A Novel Utilization of Image Registration Techniques to Process Mastcam Images in Mars Rover With Applications to Image Fusion, Pixel Clustering, and Anomaly Detection , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Chiman Kwan,et al.  Application of Deep Belief Network to Land Cover Classification Using Hyperspectral Images , 2017, ISNN.

[13]  X. Zhao,et al.  Target Detection with Improved Image Texture Feature Coding Method and Support Vector Machine , 2008 .

[14]  Jing Wang,et al.  A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Chiman Kwan,et al.  Deep Learning with Synthetic Hyperspectral Images for Improved Soil Detection in Multispectral Imagery , 2018, 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[16]  Rui Guo,et al.  Hyperspectral Anomaly Detection Through Spectral Unmixing and Dictionary-Based Low-Rank Decomposition , 2018, IEEE Transactions on Geoscience and Remote Sensing.