Phd dissertation in remote sensing


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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.

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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.


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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. A novel computational imaging approach to sensor protection based on point spread function PSF engineering is designed to suppress harmful laser irradiance without significant loss of image fidelity of a background scene.

PSF engineering is accomplished by modifying a traditional imaging system with a lossless linear phase masks at the pupil which diffracts laser light over a large area of the imaging sensor. The approach provides the additional advantage of an instantaneous response time across a broad region of the electromagnetic spectrum. As the mask does not discriminate between the laser and desired scene, a post-processing image reconstruction step is required, which may be accomplished in real time, that both removes the laser spot and improves the image fidelity.

This thesis includes significant experimental and numerical advancements in the determination and demonstration of optimized phase masks. Analytic studies of PSF engineering systems and their fundamental limits were conducted. An experimental test-bed was designed using a spatial light modulator to create digitally-controlled phase masks to image a target in the presence of a laser source.

Experimental results using already known phase masks: axicon, vortex and cubic are reported. Broadband performance of optimized phase masks were also evaluated in simulation. Optimized phase masks were shown to provide three orders of magnitude laser suppression while simultaneously providing high fidelity imaging a background scene. Earth observation through remote sensing images enables the accurate characterization of materials and objects on the surface from space and airborne platforms.

As a result, multi-sensor semantic segmentation stands out as a demanded technique in order to fully leverage complementary imaging modalities.

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The fusion of these two modalities optical imagery and LiDAR data usually can be performed at the feature level or decision level. Our research first investigated the feature level fusion that combines hand-crafted features derived from both optical imagery and LiDAR data. We then feed the combined features into various classifiers, and the results show clear advantages of using fused features.

The pixel-wise classification results are then followed by the higher-order conditional random fields CRFs to eliminate noisy labels and enforce label consistency and coherence within one segment or between segments. As the recent use of pre-trained deep convolutional neural networks DCNNs for remote sensing image classification has been extremely successful, we proposed a decision-level fusion approach that trains one DCNN for optical imagery and one linear classifier for LiDAR data.

These two probabilistic outputs are then combined later in various CRF frameworks e.

Yinan He Successfully defends his PhD dissertation

We found in the extensive experiments that the proposed decision level fusion compares favorably or outperforms the state-of-the-art baseline methods that utilize feature level fusion. Harmful cyanobacteria blooms have been increasing in frequency throughout the world resulting in a greater need for water quality monitoring. In this work, the utility of Landsat to retrieve concentrations of two cyanobacteria bloom pigments, chlorophyll-a and phycocyanin, is assessed. Concentrations of these pigments are retrieved using a spectral Look-Up-Table LUT matching process, where an exploration of the effects of LUT design on retrieval accuracy is performed.

Potential augmentations to the spectral sampling of Landsat are also tested to determine how it can be improved for waterbody constituent concentration retrieval. Applying the LUT matching process to Landsat 8 imagery determined that concentrations of chlorophyll-a, total suspended solids, and color dissolved organic matter were retrieved with a satisfactory accuracy through appropriate choice of atmospheric compensation and LUT design, in agreement with previously reported implementations of the LUT matching process.

Phycocyanin proved to be a greater challenge to this process due to its weak effect on waterbody spectrum, the lack of Landsat spectral sampling over its predominant spectral feature, and error from atmospheric compensation. This performance further improves when sampling is added to both regions, and when Landsat is transitioned to a VNIR imaging spectrometer, though this is dependent on band position and spacing. These results imply that Landsat can be used to monitor cyanobacteria blooms through retrieval of chlorophyll-a, and this retrieval performance can be improved in future Landsat systems, even with minor changes to spectral sampling.

This includes improvement in retrieval of phycocyanin when implementing a VNIR imaging spectrometer. It has been shown that a mild water deficit in grapevine contributes to wine quality, in terms of especially flavor. Water deficit irrigation and selective harvesting are implemented to optimize quality, but these approaches require rigorous measurement of vine water status.

While traditional in-field physiological measurements have made operational implementation onerous, modern small unmanned aerial systems sUAS have presented the unique opportunity for rigorous management across vast areas. This study sought to fuse hyperspectral remote sensing, sUAS, and sound multivariate analysis techniques for the purposes of assessing grapevine water status.

We demonstrated statistically significant trends in our experiment, further qualifying the potential of hyperspectral data, collected via sUAS, for the purposes of grapevine water management. There was indication that the chlorophyll and carotenoid absorption regions in the VNIR, as well as several SWIR water band regions warrant further exploration. This work was limited since we did not have access to experimentally-controlled vineyard plots, and it therefore is recommended that future work includes a full range of water stress scenarios. Remote sensing techniques are continuously being developed to extract physical information about the Earth's surface.

Over the years, space-borne and airborne sensors have been used for the characterization of surface sediments.

"Integration of Remote Sensing and Proximal Sensing for Improvement of " by Foad Foolad

Spectral observations of sediments can be used to effectively identify the physical characteristics of the surface. Geophysical properties of a sediment surface such as its density, grain size, surface roughness, and moisture content can influence the angular dependence of spectral signatures, specifically the Bidirectional Reflectance Distribution Function BRDF. Models based on radiative transfer equations can relate the angular dependence of the reflectance to these geophysical variables.

Extraction of these parameters can provide a better understanding of the Earth's surface, and play a vital role in various environmental modeling processes. In this work, we focused on retrieving two of these geophysical properties of earth sediments, the bulk density and the soil moisture content SMC , using directional hyperspectral reflectance. We proposed a modification to the radiative transfer model developed by Hapke to retrieve sediment bulk density.

The model was verified under controlled experiments within a laboratory setting, followed by retrieval of the sediment density from different remote sensing platforms: airborne, space-borne and a ground-based imaging sensor.


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The Fabry-Perot interferometer FPI is a well-developed and widely used tool to control and measure wavelengths of light. In optical imaging applications, there is often a need for systems with compact, integrated, and widely tunable spectral filtering capabilities. This array of individually tunable FPIs have been designed to operate across the visible light spectrum from nm. This design can give rise to a new line of compact spectrometers with fewer moving parts and the ability to perform customizable filtering schemes at the hardware level.

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The original design was modeled, simulated, and fabricated but not tested and evaluated. We perform optical testing on the fabricated devices to measure the spectral resolution and wavelength tunability of these FP etalons. We collect the transmission spectrum through the FP etalons to evaluate their quality, finesse, and free spectral range. We then attempt to thermally actuate the expansion mechanisms in the FP cavity to validate tunability across the visible spectrum. Unfortunately, metal thin film stress and step coverage issues resulted in device heater failures, preventing actuation.

This FP filter array design proves to be a viable manufacturing design for an imaging focal plane with individually tunable pixels.

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However, it will require more optimization and extensive electrical, optical, thermal, and mechanical testing when integrated with a detector array. The study of human vision must include our interaction with objects. These studies can include behavior modeling, understanding visual attention, and motor guidance, and enhancing user experiences. But all these studies have one thing in common. To analyze the data in detail, researchers typically have to analyze video data frame by frame. Real world interaction data often comprises of data from both eye and hand.

Analyzing such data frame by frame can get very tedious and time-consuming.

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