Illumination Invariant Deep Learning for Hyperspectral Data

Motivated by the variability in hyperspectral images due to illumination and the difficulty in acquiring labelled data, this thesis proposes different approaches for learning illumination invariant feature representations and classification models for hyperspectral data captured outdoors, under natural sunlight. The approaches integrate domain knowledge into learning algorithms and hence does not rely on a priori knowledge of atmospheric parameters, additional sensors or large amounts of labelled training data. Hyperspectral sensors record rich semantic information from a scene, making them useful for robotics or remote sensing applications where perception systems are used to gain an understanding of the scene. Images recorded by hyperspectral sensors can, however, be affected to varying degrees by intrinsic factors relating to the sensor itself (keystone, smile, noise, particularly at the limits of the sensed spectral range) but also by extrinsic factors such as the way the scene is illuminated. The appearance of the scene in the image is tied to the incident illumination which is dependent on variables such as the position of the sun, geometry of the surface and the prevailing atmospheric conditions. Effects like shadows can make the appearance and spectral characteristics of identical materials to be significantly different. This degrades the performance of high-level algorithms that use hyperspectral data, such as those that do classification and clustering. If sufficient training data is available, learning algorithms such as neural networks can capture variability in the scene appearance and be trained to compensate for it. Learning algorithms are advantageous for this task because they do not require a priori knowledge of the prevailing atmospheric conditions or data from additional sensors. Labelling of hyperspectral data is, however, difficult and time-consuming, so acquiring enough labelled samples for the learning algorithm to adequately capture the scene appearance is challenging. Hence, there is a need for the development of techniques that are invariant to the effects of illumination that do not require large amounts of labelled data. In this thesis, an approach to learning a representation of hyperspectral data that is invariant to the effects of illumination is proposed. This approach combines a physics-based model of the illumination process with an unsupervised deep learning algorithm, and thus requires no labelled data. Datasets that vary both temporally and spatially are used to compare the proposed approach to other similar state-of-the-art techniques. The results show that the learnt representation is more invariant to shadows in the image and to variations in brightness due to changes in the scene topography or position of the sun in the sky. The results also show that a supervised classifier can predict class labels more accurately and more consistently across time when images are represented using the proposed method. Additionally, this thesis proposes methods to train supervised classification models to be more robust to variations in illumination where only limited amounts of labelled data are available. The transfer of knowledge from well-labelled datasets to poorly labelled datasets for classification is investigated. A method is also proposed for enabling small amounts of labelled samples to capture the variability in spectra across the scene. These samples are then used to train a classifier to be robust to the variability in the data caused by variations in illumination. The results show that these approaches make convolutional neural network classifiers more robust and achieve better performance when there is limited labelled training data. A case study is presented where a pipeline is proposed that incorporates the methods proposed in this thesis for learning robust feature representations and classification models. A scene is clustered using no labelled data. The results show that the pipeline groups the data into clusters that are consistent with the spatial distribution of the classes in the scene as determined from ground truth

Detection, Segmentation, and Model Fitting of Individual Tree Stems from Airborne Laser Scanning of Forests Using Deep Learning

Accurate measurements of the structural characteristics of trees such as height, diameter, sweep and taper are an important part of forest inventories in managed forests and commercial plantations. Both terrestrial and aerial LiDAR are currently employed to produce pointcloud data from which inventory metrics can be determined. Terrestrial/ground-based scanning typically provides pointclouds resolutions of many thousands of points per m 2 from which tree stems can be observed and inventory measurements made directly, whereas typical resolutions from aerial scanning (tens of points per m 2 ) require inventory metrics to be regressed from LiDAR variables using inventory reference data collected from the ground. Recent developments in miniaturised LiDAR sensors are enabling aerial capture of pointclouds from low-flying aircraft at high-resolutions (hundreds of points per m 2 ) from which tree stem information starts to become directly visible, enabling the possibility for plot-scale inventories that do not require access to the ground. In this paper, we develop new approaches to automated tree detection, segmentation and stem reconstruction using algorithms based on deep supervised machine learning which are designed for use with aerially acquired high-resolution LiDAR pointclouds. Our approach is able to isolate individual trees, determine tree stem points and further build a segmented model of the main tree stem that encompasses tree height, diameter, taper, and sweep. Through the use of deep learning models, our approach is able to adapt to variations in pointcloud densities and partial occlusions that are particularly prevalent when data is captured from the air. We present results of our algorithms using high-resolution LiDAR pointclouds captured from a helicopter over two Radiata pine forests in NSW, Australia

Unsupervised Feature-Learning for Hyperspectral Data with Autoencoders

This paper proposes novel autoencoders for unsupervised feature-learning from hyperspectral data. Hyperspectral data typically have many dimensions and a significant amount of variability such that many data points are required to represent the distribution of the data. This poses challenges for higher-level algorithms which use the hyperspectral data (e.g., those that map the environment). Feature-learning mitigates this by projecting the data into a lower-dimensional space where the important information is either preserved or enhanced. In many applications, the amount of labelled hyperspectral data that can be acquired is limited. Hence, there is a need for feature-learning algorithms to be unsupervised. This work proposes unsupervised techniques that incorporate spectral measures from the remote-sensing literature into the objective functions of autoencoder feature learners. The proposed techniques are evaluated on the separability of their feature spaces as well as on their application as features for a clustering task, where they are compared against other unsupervised feature-learning approaches on several different datasets. The results show that autoencoders using spectral measures outperform those using the standard squared-error objective function for unsupervised hyperspectral feature-learning

Automated Mapping of Woody Debris over Harvested Forest Plantations Using UAVs, High-Resolution Imagery, and Machine Learning

Surveying of woody debris left over from harvesting operations on managed forests is an important step in monitoring site quality, managing the extraction of residues and reconciling differences in pre-harvest inventories and actual timber yields. Traditional methods for post-harvest survey involving manual assessment of debris on the ground over small sample plots are labor-intensive, time-consuming, and do not scale well to heterogeneous landscapes. In this paper, we propose and evaluate new automated methods for the collection and interpretation of high-resolution, Unmanned Aerial Vehicle (UAV)-borne imagery over post-harvested forests for estimating quantities of fine and coarse woody debris. Using high-resolution, geo-registered color mosaics generated from UAV-borne images, we develop manual and automated processing methods for detecting, segmenting and counting both fine and coarse woody debris, including tree stumps, exploiting state-of-the-art machine learning and image processing techniques. Results are presented using imagery over a post-harvested compartment in a Pinus radiata plantation and demonstrate the capacity for both manual image annotations and automated image processing to accurately detect and quantify coarse woody debris and stumps left over after harvest, providing a cost-effective and scalable survey method for forest managers

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

The remote mapping of minerals and discrimination of ore and waste on surfaces are important tasks for geological applications such as those in mining. Such tasks have become possible using ground-based, close-range hyperspectral sensors which can remotely measure the reflectance properties of the environment with high spatial and spectral resolution. However, autonomous mapping of mineral spectra measured on an open-cut mine face remains a challenging problem due to the subtleness of differences in spectral absorption features between mineral and rock classes as well as variability in the illumination of the scene. An additional layer of difficulty arises when there is no annotated data available to train a supervised learning algorithm. A pipeline for unsupervised mapping of spectra on a mine face is proposed which draws from several recent advances in the hyperspectral machine learning literature. The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face without the need for human-annotated training data. The pipeline is evaluated with a hyperspectral image dataset of an open-cut mine face comprising mineral ore martite and non-mineralised shale. The combined system is shown to produce a superior map to its constituent algorithms, and the consistency of its mapping capability is demonstrated using data acquired at two different times of day