Autonomous agents often require accurate methods for detecting and localizing changes in their environment, particularly when observations are captured from unconstrained and inconsistent viewpoints. We propose a novel label-free, pose-agnostic change detection method that integrates information from multiple viewpoints to construct a change-aware 3D Gaussian Splatting (3DGS) representation of the scene. With as few as 5 images of the post-change scene, our approach can learn an additional change channel in a 3DGS and produce change masks that outperform single-view techniques. Our change-aware 3D scene representation additionally enables the generation of accurate change masks for unseen viewpoints. Experimental results demonstrate state-of-the-art performance in complex multi-object scenes, achieving a 1.7× and 1.5× improvement in Mean Intersection Over Union and F1 score respectively over other baselines. We also contribute a new real-world dataset to benchmark change detection in diverse challenging scenes in the presence of lighting variations. Our code and the dataset are available at MV-3DCD.github.io.

This paper presents an autonomous approach to tree detection and segmentation from high resolution airborne LiDAR pointclouds, such as those collected from a UAV, that utilises region-based CNN and 3D-CNN deep learning algorithms. Trees are first detected in 2D before individual trees are further characterised in 3D. If the number of training examples for a site is low, it is shown to be beneficial to transfer a segmentation network learnt from a different site with more training data and fine-tune it. The algorithm was validated using airborne laser scanning over two different commercial pine plantations. The results show that the proposed approach performs favourably in comparison to other methods for tree detection and segmentation.

Convolutional neural networks (CNNs) have been shown to be a powerful tool for image classification. Recently, they have been adopted into the remote sensing community with applications in material classification from hyperspectral images. However, CNNs are time-consuming to train and often require large amounts of labeled training data. The widespread use of CNNs in the image processing and computer vision communities has been facilitated by the networks that have already been trained on large amounts of data. These pretrained networks can be used to initialize networks for new tasks. This transfer of knowledge makes it far less time-consuming to train a new classifier and reduces the need for a large labeled data set. This concept of transfer learning has not yet been fully explored by those using CNNs to train material classifiers from hyperspectral data. This paper provides an insight into training hyperspectral CNN classifiers by transferring knowledge from well labeled data sets to data sets that are less well labeled. It is shown that these CNNs can transfer between completely different domains and sensing platforms, and still improve classification performance. The application of this work is in the training of material classifiers of data acquired from field-based platforms, by transferring knowledge from publicly accessible airborne data sets. Factors, such as training set size, CNN architectures, and the impact of filter width and wavelength interval, are studied.

This paper proposes the Relit Spectral Angle-Stacked Autoencoder, a novel unsupervised feature learning approach for mapping pixel reflectances to illumination invariant encodings. This work extends the Spectral Angle-Stacked Autoencoder so that it can learn a shadow-invariant mapping. The method is inspired by a deep learning technique, Denoising Autoencoders, with the incorporation of a physics-based model for illumination such that the algorithm learns a shadow invariant mapping without the need for any labelled training data, additional sensors, a priori knowledge of the scene or the assumption of Planckian illumination. The method is evaluated using datasets captured from several different cameras, with experiments to demonstrate the illumination invariance of the features and how they can be used practically to improve the performance of high-level perception algorithms that operate on images acquired outdoors.

The illumination conditions of a scene create intra-class variability in outdoor visual data, degrading the performance of high-level algorithms. Using only the image, and with hyper-spectral data as a case study, this paper proposes a deep learning approach to learn illumination invariant features from the data in an unsupervised manner. The proposed approach incorporates a similarity measure, the Spectral Angle, that is relatively insensitive to brightness into the cost function of a Stacked Auto-Encoder so that an illumination invariant mapping is learned from the input data to the hidden layer. Experiments using synthetic and real imagery show that this novel feature learning approach produces a more illumination invariant representation of the data, improving the results of a high-level algorithm (clustering) under such conditions.