4 research outputs found
Spectral-spatial Feature Extraction for Hyperspectral Image Classification
As an emerging technology, hyperspectral imaging provides huge
opportunities in both remote sensing and computer vision. The
advantage of hyperspectral imaging comes from the high resolution
and wide range in the electromagnetic spectral domain which
reflects the intrinsic properties of object materials. By
combining spatial and spectral information, it is possible to
extract more comprehensive and discriminative representation for
objects of interest than traditional methods, thus facilitating
the basic pattern recognition tasks, such as object detection,
recognition, and classification. With advanced imaging
technologies gradually available for universities and industry,
there is an increased demand to develop new methods which can
fully explore the information embedded in hyperspectral images.
In this thesis, three spectral-spatial feature extraction methods
are developed for salient object detection, hyperspectral face
recognition, and remote sensing image classification.
Object detection is an important task for many applications based
on hyperspectral imaging. While most traditional methods rely on
the pixel-wise spectral response, many recent efforts have been
put on extracting spectral-spatial features. In the first
approach, we extend Itti's visual saliency model to the spectral
domain and introduce the spectral-spatial distribution based
saliency model for object detection. This procedure enables the
extraction of salient spectral features in the scale space, which
is related to the material property and spatial layout of
objects.
Traditional 2D face recognition has been studied for many years
and achieved great success. Nonetheless, there is high demand to
explore unrevealed information other than structures and textures
in spatial domain in faces. Hyperspectral imaging meets such
requirements by providing additional spectral information on
objects, in completion to the traditional spatial features
extracted in 2D images. In the second approach, we propose a
novel 3D high-order texture pattern descriptor for hyperspectral
face recognition, which effectively exploits both spatial and
spectral features in hyperspectral images. Based on the local
derivative pattern, our method encodes hyperspectral faces with
multi-directional derivatives and binarization function in
spectral-spatial space. Compared to traditional face recognition
methods, our method can describe distinctive micro-patterns which
integrate the spatial and spectral information of faces.
Mathematical morphology operations are limited to extracting
spatial feature in two-dimensional data and cannot cope with
hyperspectral images due to so-called ordering problem. In the
third approach, we propose a novel multi-dimensional morphology
descriptor, tensor morphology profile~(TMP), for hyperspectral
image classification. TMP is a general framework to extract
multi-dimensional structures in high-dimensional data. The
n-order morphology profile is proposed to work with the n-order
tensor, which can capture the inner high order structures. By
treating a hyperspectral image as a tensor, it is possible to
extend the morphology to high dimensional data so that powerful
morphological tools can be used to analyze hyperspectral images
with fused spectral-spatial information.
At last, we discuss the sampling strategy for the evaluation of
spectral-spatial methods in remote sensing hyperspectral image
classification. We find that traditional pixel-based random
sampling strategy for spectral processing will lead to unfair or
biased performance evaluation in the spectral-spatial processing
context. When training and testing samples are randomly drawn
from the same image, the dependence caused by overlap between
them may be artificially enhanced by some spatial processing
methods. It is hard to determine whether the improvement of
classification accuracy is caused by incorporating spatial
information into the classifier or by increasing the overlap
between training and testing samples. To partially solve this
problem, we propose a novel controlled random sampling strategy
for spectral-spatial methods. It can significantly reduce the
overlap between training and testing samples and provides more
objective and accurate evaluation
Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles
Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE