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DIRS Laboratory 76-3215
September 9, 2019 at 10:45am
Ph.D. Thesis Defense
With rapid developments in satellite and sensor technologies, there has been a dramatic increase in the availabilities of remotely sensed images obtained with different modalities. Given these data, there is always an urgent need for developing automatic algorithms that help experts with better image analyzing capabilities. In this work, we explore techniques related to object detection in both high resolution aerial images and hyperspectral remote sensing images.
In the first part of the thesis, subpixel object detection in hyperspectral images was studied. We propose a novel image segmentation algorithm to identify spatial-spectral coherent image regions, from which the background statistics were estimated for deriving the MFs. The proposed method is accompanied by extensive experimental studies that corroborate its merits.
The second part of the thesis explores the object based image analysis (OBIA) approach for object detection in high resolution aerial images. We formulate the detection problem into a tree-matching framework and propose two tree-matching algorithms. Our results demonstrate efficiency and advantages of the detection framework.
At last, we study object detection in high resolution aerial images from a machine learning perspective. We investigate both traditional machine learning based and end-to-end convolutional neural network (CNN) based approaches for various detection tasks. In traditional detection framework, we propose to apply the Gaussian process classifier (GPC) to train an object detector. In the CNN based approach, we proposed a novel scale transfer module that generates better feature maps for object detection. Our results show the efficiency of the proposed method and competitiveness when compared to state-of-the-art counterparts.