Light detection and ranging (LiDAR) remote sensing systems are deployed in various platforms including satellites, airplanes, and drones—which, in essence, determines the sampling characteristics of the underlying imaging system. Low-altitude LiDARs provide high photon count and high spatial resolution but only in very localized patches. Satellite LiDARs, on the other hand, provide measurements at a global scale but are limited by low photon count and their samples are sparsely apart along swath line trajectories that are far in between. This article describes a new class of satellite remote sensing LiDARs, aimed at overcoming the limitations of current satellite imaging systems. It exploits the principles of compressive sensing and machine learning (ML) to compressively sense Earth from hundreds of kilometers above Earth to then reconstruct the 3-D imagery with resolution and coverage, as if the data were collected from airborne platforms at just hundreds of meters in height. We introduce a novel representation of waveform altimetry profiles, coined hyperheight data cubes (HHDCs), which encompass rich information about the 3-D structure of a scene. Canopy height models (CHMs), digital terrain models (DTMs), and many other features of a scene that are embedded in HHDC are easily extracted with simple statistical quantiles. We introduce ML methods to reconstruct the compressive LiDAR measurements so as to attain high-resolution, dense coverage, and broad field-of-view per swath pass. ML training data are attained from NASA’s G-LiHT imaging missions. Simulations with various types of forests across the US illustrate the power of the new LiDAR imaging systems.

Spaceborne lidars are essential for monitoring Earth’s ecosystems, particularly in imaging forests, glaciers, and natural hazards. However, current satellite lidar systems, such as NASA’s GEDI and ICESat-2, are limited in spatial resolution and photon density, constraining their ability to capture detailed Surface Topography and Vegetation (STV) 3D imagery. Airborne systems, like NASA’s G-LiHT, offer higher resolution but lack global coverage. To address these limitations, compressive satellite lidars (CS-Lidars) have been recently introduced, utilizing coded laser illumination and dynamic wavelength scanning for wide-field 3D imaging. A novel framework, based on HyperHeight Data Cubes (HHDCs), uses deep learning to transform sparse measurements into 3D images, but its resolution remains constrained by physical limitations of the instruments. This paper proposes three approaches using generative diffusion models to achieve super-resolution lidar imaging, enhancing satellite data resolution. These methods involve learning conditional probabilities, guiding models via forward imaging, and leveraging high-resolution side information. The results show substantial improvements in the resolution of satellite lidar data, enabling finer-scale studies of forest structure and improving applications in forest management and environmental monitoring. The methodologies were tested in three regions of the United States: Florida, Maryland, and California. The models were trained and tested on the first two, and their zero-shot capabilities were tested on the third, showing comparable results.

Spaceborne LiDAR systems are crucial for Earth observation but face hardware constraints, thus limiting resolution and data processing. We propose integrating compressed sensing and diffusion generative models to reconstruct high-resolution satellite LiDAR data within the Hyperheight Data Cube (HHDC) framework. Using a randomized illumination pattern in the imaging model, we achieve efficient sampling and compression, reducing the onboard computational load and optimizing data transmission. Diffusion models then reconstruct detailed HHDCs from sparse samples on Earth. To ensure reliability despite lossy compression, we analyze distortion metrics for derived products like Digital Terrain and Canopy Height Models and evaluate the 3D reconstruction accuracy in waveform space. We identify image quality assessment metrics—ADD_GSIM, DSS, HaarPSI, PSIM, SSIM4, CVSSI, MCSD, and MDSI—that strongly correlate with subjective quality in reconstructed forest landscapes. This work advances high-resolution Earth observation by combining efficient data handling with insights into LiDAR imaging fidelity.

Compressive satellite LiDAR (CS-LiDAR) has been recently introduced as a radically different computational sensing and reconstruction approach for LiDAR sensing of Earth. It is based on NASA’s adaptive wavelength scanning LiDAR (AWSL) system. Rather than measuring 1D line footprints over a satellite’s swath path as is the norm today, CS-LiDAR adopts sparse coded laser illumination over a 2D wide field-of-view. The objective is to compressively sense Earth from hundreds of km above Earth to then computationally reconstruct the 3D imagery with resolution and coverage as if the data was collected from just hundreds of meters in height. The forward imaging model captures the light propagation phenomena affecting the photon pulses transmitted from the sensor to the Earth’s surface and back. This paper advances CS-LiDAR on many fronts. First, imaging spectroscopy side-information, often jointly available with LiDARs, is integrated into a multimodal imaging system. Secondly, the inverse imaging problem is cast under a transformer network architecture driven by multimodal attention mechanisms. Finally, by directing the snapshot spectral cameras in front of the LiDAR, the transformer mechanisms autonomously adjust the LiDAR’s beam scanning to focus on specific target locations, thus attaining end-to-end optimal adaptive sampling that can respond to varying observational conditions, surface events, and scientific priorities. Emulations enabled by enormous observational LiDAR data of Earth, available from NASA’s G-LiHT imaging observatory, show the advantages attained by methods developed in this work.

Coded aperture snapshot spectral imager (CASSI) senses the spectral information of a 2D scene and captures a set of coded measurement data that can be used to reconstruct the 3D spatio-spectral datacube of the input scene by compressive sensing algorithms. The coded aperture (CA) in CASSI plays a crucial role in modulating the spatial information. The pixels in CA are typically square, switched binary on-off, and aligned with the pixels of focal plane array (FPA). Instead of this binary modulation, this paper explores a simple yet effective approach to enabling an equivalent grayscale modulation, which can increase the sensing degree of freedom in CASSI systems. In particular, we deliberately introduce misalignment between the CA pixels and the FPA pixels, such that the spatial modulation of one FPA pixel is determined by four adjacent CA pixels instead of one. Numerical experiments show that the proposed equivalent grayscale modulation induced by misalignment can significantly improve the CASSI reconstruction when compared with current methods, whether a random CA or an optimal blue noise CA is used. More importantly, it does not incur in any cost to the CASSI system.

Sensing the Earth’s surface topography and vegetation (STV) structure is of critical importance for a myriad of scientific applications. STV metrology relies on lidar, radar, stereophotogrammetry, or a combination of these remote sensing techniques. STV metrology, however, suffers from low spatial and height resolution or sparse coverage if lidars and stereophotogrammetry are deployed at orbital heights. Many scientific applications such as bare Earth, cryosphere, and hydrology, require meter or sub-meter STV observables in spatial resolution with submeter vertical resolution. This work aims to overcome the STV resolution gap by using a simple observation system composed of an orbital Compressive Sensing (CS) lidar aided by high-resolution monocular RGB photography. The system first produces a super-resolved digital surface model by fusing satellite CS lidar photon returns with monocular photography using an image-to-image translation generative Brownian Bridge Diffusion Model. Subsequently, the low photon count lidar measurements together with the high-resolution DSM are then used in a constrained Denoising Diffusion Probabilistic Model to reconstruct super-resolved, wall-to-wall, and feature-rich HyperHeight STV Data Cubes. This approach effectively enhances the resolution for satellite LiDAR imagery while reducing generative model hallucinations, thereby improving the reliability and utility of the resulting data products for Earth studies. The achievable spatial resolution depends on the monocular RGB imagery resolution, the photon density of the training point cloud, and the noise level in the LiDAR sensor.