Feature learning of airborne laser scanning (ALS) point clouds is challenged by both the limited annotated samples and imbalanced class distribution. An intuitive way involves pretraining on a well-annotated source dataset and fine-tuning on a limited target dataset. However, cross-domain challenges such as heterogeneous point cloud density, varying terrain features, and inconsistent object categories complicate transfer learning for 3-D land cover classification. In this article, we address these issues by separating the cross-domain ALS point cloud semantic segmentation into two subsequent subtasks, i.e., the cross-domain transfer learning subtask and the intradomain class-incremental learning subtask, and we use a well-annotated photogrammetric point cloud dataset as the source dataset. To mitigate domain discrepancies, the first subtask employs domain adversarial training to learn from base categories that are shared between source and target datasets. Then, the second subtask incrementally learns new categories that are specific within the target dataset using an incremental feature-semantic distillation module and a semantic adversarial learning module while retaining base category knowledge. Experimental results evaluated on three ALS point cloud datasets (ISPRS, DALES, and H3D) with different semantics show state-of-the-art cross-domain performance with few labeled samples. Compared with few-shot learning methods, our method shows promising generalization ability particularly on domain-specific categories, greatly alleviating the dependence on ALS point cloud annotations.

Dense matching is an important step in radargrammetry. As approximating epipolar lines in synthetic aperture radar (SAR) images is difficult, conventional dense image matching (DIM) algorithms are unsuitable for these images. This study proposes a DIM algorithm for unmanned aerial vehicle (UAV) SAR images that does not require epipolar rectification. The proposed algorithm uses tie-point matching results to construct a search window for corresponding points and utilizes an improved DAISY descriptor incorporating the ratio of exponentially weighted averages operator for cost calculation, which suppresses errors caused by speckle noise. Cost aggregation was performed using computationally efficient superpixel segmentation and the guided filter algorithm, and thewinner-takes-allstrategy was applied for dense matching. Finally, experiments were performed on six pairs of UAV SAR images containing different terrains and ground objects, and an average root mean square error of 4.3 pixels was obtained, demonstrating that the proposed method is superior to conventional DIM algorithms and has excellent precision and accuracy.

LiDAR diffuse attenuation coefficient (${K}_{\text {lidar}}$) describes the laser attenuation degree in water, and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is expected to extend the observation of this parameter to satellites. However, the ICESat-2 observation is limited to the satellite trajectories, and it is hard to realize the coverage observation of inland water. Therefore, this study proposes a method fusing active and passive remote sensing data based on machine learning and uses ICESat-2 data and Sentinel-3 images to retrieve$K_{\text {lidar}}$of inland waters. First,$K_{\text {lidar}}$is retrieved from ICESat-2 water column photons. Second, the retrieved$K_{\text {lidar}}$and Sentinel-3 images are used to train machine learning models to generate$K_{\text {lidar}}$covering water bodies. Experiments were carried out in Xiaolangdi Reservoir using multitemporal data. The results show that the performance of CatBoost is better than that of random forest (RF) and XGBoost, and the consistency reaches 68.42%. Compared with in situ data, the root mean square error (RMSE) of the proposed method is$0.0555~\text {m}^{-1}$, which is 61.05% higher than the RMSE of the Sentinel-3 product. By combining laser data with multispectral images, water quality observation close to field measurement can be retrieved from satellite platforms.

Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, road updating process is imperative to maintain the high quality of current basic geographic information. Currently, road extraction and change detection are two commonly used methods to solve road updating problems. Most of the existing methods rely on a large number of accurate road labels to generate road information, while ignoring the use of quantities of available but incomplete road maps. In our work, we proposed a semi-supervised road extraction method specifically for road-updating applications (SRUNet). In this approach, historical road maps are fused with the latest remote sensing images, and state of the roads are updated directly. A multi-branch network is the core of the method, which consists of three noteworthy parts: Map Encoding Branch (MEB) proposed for representation learning, Boundary Enhancement Module (BEM) for improving the accuracy of boundary prediction, and Residual Refinement Module (RRM) for further optimizing the prediction results. We applied our method to two datasets: the DeepGlobe public dataset and our self-constructed dataset from Zhengzhou and Nanjing. Experimental results shows that our method achievs an improvement of 14.37% over the baseline approach. Notably, the addition of historical maps improved the model’s performance by 12.4%. Promising results were obtained on two cities’ large-scale road networks. With the reliable prediction results and improved performance, we believe SRUNet is meaningful for a wide range of road renewal applications.

Existing airborne laser scanning (ALS) point cloud semantic segmentation approaches are limited by their overreliances on sufficient point-wise annotations that further confine their generalization ability to new scenes. To overcome these problems, a novel three-stage multi-prototype relational network (Thr-MPRNet) is proposed for few-shot ALS point cloud semantic segmentation by transferring knowledge from well-annotated photogrammetric point clouds. In MPRNet, a 3D few-shot learning structure containing a feature learner and a relation learner is built to learn meta-knowledge from multiple point-wise tasks, and a multi-prototype generator is designed to represent the semantic distribution of point clouds that can dynamically adapt to large-scale scenarios. Then, to transfer knowledge across different domains, MPRNet is trained in a unified framework with three task-based learning stages. Prior knowledge is first meta-learned from the source photogrammetric point clouds and then transferred to novel target datasets with a few labeled ALS point clouds. Finally, the MPRNet can be flexibly generalized to the unlabeled target ALS point clouds without further retraining from scratch. In the experiments, the SensatUrban dataset is used as the source photogrammetric point clouds, and two ALS point cloud datasets (ISPRS and DALES) are used to evaluate the few-shot semantic segmentation ability of the proposed method. The experiments demonstrate that Thr-MPRNet obtains promising generalization performance on different target datasets. More importantly, it outperforms supervised networks with 10% labeled samples. In summary, the proposed method achieves state-of-the-art cross-domain semantic segmentation performance and greatly alleviates the dependence on ALS point cloud annotations.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) equips with a novel photon-counting LiDAR system, which can generate underwater reflections in nearshore environments. However, due to the water reflection, scattering, and absorption, the distribution of bathymetric photons in the nearshore data varies with depth. The existing bathymetric photon extraction algorithms need more adaptability to seafloor topography. The changing density of bathymetric photons and the fluctuation of underwater topography make the noise removal of nearshore data full of challenges. This study proposed a bathymetric photon extraction algorithm using pre-pruning quadtree isolation (PQI). Firstly, the pre-pruning step judges whether to stop the growth of quadtree in advance during quadtree isolation (QI) to avoid excessive division of noise photons. Secondly, the maximum inter-class variance algorithm (also called the Otsu method) obtains the best threshold of isolation depth and extracts bathymetric photons. The algorithm was tested on the Florida coast. The results show that the PQI algorithm can wholly and accurately extract bathymetric photons with different acquisition times from the data. The F1-score of the extracted results is 93.96%. This study provides an intelligent solution to processing bathymetric data in nearshore environments worldwide.

The detection and recognition of oriented objects in remote sensing images is a challenging task due to their complex backgrounds, various sizes, diverse aspect ratios, and especially arbitrary orientations. Many oriented object detection algorithms need to obtain accurate angles or adopt anchors to predict the oriented bounding boxes. When directly predicting the angles of objects’ oriented bounding boxes, the loss of angle is discontinuous during training, which makes it difficult to obtain accurate boundary of oriented objects. And the anchors also aggravate the problems of class imbalance and computational cost. To address the above problems, this paper proposes an anchor-free and angle-free detector called AF2Det. AF2Det adopts the information of the bounding box projection instead of the angle to represent and reconstruct the object’s oriented bounding boxes, which could avoid the problem of boundary discontinuity. To predict the information of the bounding box projection, an anchor-free architecture is built to predict objects as points based on a simple but strong U-shaped architecture. And the deformable convolution and the bottom-up feature fusion method are integrated effectively to enhance AF2Det’ s capacity for objects’ shapes, orientations, and scales. The extensive experiments are conducted on multiple datasets, i.e., HRSC2016, FGSD2021, DOTA, and RSDD-SAR to validate the effectiveness of our method. The experimental results demonstrate that the proposed AF2Det outperforms other anchor-free algorithms and obtains competitive results on oriented object detection.

Photon-counting light detection and ranging (LiDAR) Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) enables the drafting of global elevation maps. However, vegetation cover, terrain undulation, and residual noise in signal photons substantially reduce the accuracy of ground photon extraction. Existing ground photon extraction algorithms do not consider the factors influencing photon extraction, and the threshold setting lacks a theoretical basis. This study proposed a photon-extraction algorithm with scenario adaptability. First, the cloth simulation (CS) was adapted with a terrain index (TI) to extract ground photons; based on this, the cloth breakage concept was proposed to remove residual noise. We tested the algorithm in Denali National Park and compared its results with those of other extraction algorithms. The results showed that the TI was robust and consistent with the actual terrain; the adaptive CS achieved the best accuracy and precision under different canopy heights and terrains. The mean absolute error (MAE) and root mean square error (RMSE) of extracted photons were 0.95 and 3.41 m, respectively. This study provides a solution to estimate ground elevation using photon-counting LiDAR data.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is the world’s first satellite-borne photon-counting laser altimeter with unprecedented detection performance. Noise removal is an important process applied to raw data and determines the quality of the end product. Assuming that the sparse spatial distribution of noise photons makes them more easily isolated than signal photons, we propose a noise-removal algorithm without input parameters based on quadtree isolation. MATLAS was used to evaluate the performance of our algorithm. We compare our algorithm to the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm. Experimental results show that our algorithm accurately extracts signal photons from raw data and is superior to the improved DBSCAN in accuracy and time efficiency. This novel algorithm makes it possible to efficiently remove noise from photon-counting light detection and ranging (LiDAR) data.

Planetary mapping products play a significant role in landing site selection, surface operations, and scientific investigations. This article describes the techniques and processes used to create geometrically controlled image mosaics for the candidate landing regions of China’s first Mars exploration mission (i.e., Tianwen-1) using Mars Express (MEX) High-Resolution Stereo Camera (HRSC) images. To deal with the extremely complex situations due to large data volumes (hundreds of images), various imaging conditions (e.g., 5°–80° incidence angles), and long time spans (>10 years), we developed corresponding methods and in-house software for the photogrammetric processing of MEX HRSC images. According to the characteristics of planetary images and multiline pushbroom imaging, we optimized the algorithms of establishing control network, eliminating blunders, setting weights, and processing abnormal images. The generated MEX HRSC image mosaics with a resolution of 12.5 m delivered a high relative accuracy (< 1 pixel). Compared with the existing Mars global image mosaics, the generated image mosaics exhibit significantly better spatial resolution. The techniques and methods that we developed to solve the complicated photogrammetric processing problems can also be applied to produce global or large-scale Mars mapping products using existing and new returned orbital images.

Because optical and synthetic aperture radar (SAR) images are complementary, their registration has received extensive attention in joint applications. However, robust optical and SAR image registration is challenging due to substantial geometric and radiometric differences. To address this problem, we propose a fast and robust registration algorithm for optical and SAR images based on a novel feature type known as the adjacent self-similarity (ASS). The ASS feature of the pixelwise feature representation is defined to quickly and finely capture the structural features of the image. The ASS feature is extracted by using an optimized offset mean filtering method in a neighborhood of the unit pixel radius to accelerate and refine calculations and the local statistics weighted difference operation to suppress coherent speckles. Based on the ASS feature, we extract the minimum self-similarity map (SSM) and the index map, which are robust against radiometric differences and speckles. Then, based on the excellent characteristics of the two maps, we propose a feature detector based on suppressing the local nonmaximum on the minimum SSM and a novel feature descriptor based on calculating the distribution histogram of the index map in a log-polar grid. In addition, we design a rotation invariance enhancement method for the descriptor to improve the rotation invariance robustness of the algorithm. We conduct experiments with both synthetic and real image pairs. The registration results demonstrate that the proposed algorithm has good scale and rotation invariance, as well as good antinoise ability, and that the algorithm performs better than existing state-of-the-art algorithms in terms of registration robustness, accuracy, and efficiency. The registration results on two real optical and SAR image pairs with complex image scenes show the adaptability of the proposed algorithm. The source code of ASS is publicly available1.

The spatial and seasonal variability of upper ocean NO3nutrients and chlorophyll concentration in the Eastern North Pacific (ENP) is studied with the Level 3 sea surface chlorophyll concentration data from remote sensing and the profiling data of three Bio-Geo-Chemical Argo (BGC-Argo) floats. At surface, nutrients concentration is the main factor that controls the spatial variations of chlorophyll concentration, which makes chlorophyll concentration increase polarward and coast-ward. At subsurface, high chlorophyll concentration mainly occurs from 100 m to surface at high and middle latitudes, but from 150 m to 100 m at low latitude. At high latitude, temperature is the main factor that influences chlorophyll concentration, so there is apparent seasonal variability of chlorophyll concentration. At low latitude, nutrients concentration is the main factor that influences chlorophyll concentration, so maximum chlorophyll concentration occurs at the depth of about 100-150 m, where the nutrients could reach.

Automatic optical-to-synthetic aperture radar (SAR) image matching is still a challenging task due to the existence of severe nonlinear radiometric differences between the images and the presence of strong speckles in the SAR images. To address this problem, we propose a novel feature descriptor called rank-based local self-similarity (RLSS) for optical-to-SAR image template matching. The RLSS descriptor is an improved version of the local self-similarity (LSS) descriptor, inspired by Spearman's rank correlation coefficient in statistics. It can describe the local shape properties of an image in a discriminable manner. To further improve the discriminability, a dense RLSS (DRLSS) descriptor is formed with a dense scheme by integrating the RLSS descriptors for multiple local regions into a dense sampling grid. Experimental results conducted based on the optical and SAR image pairs demonstrated that the proposed descriptor was robust to nonlinear radiometric differences and it outperformed two state-of-the-art descriptors [dense LSS (DLSS) and histogram of orientated phase congruency (HOPC)].

The digital orthophoto map of planetary bodies plays a significant role in landing site selection, traverse path planning, and scientific research. However, existing orthorectification methods in the planetary mapping community exhibit low-computational efficiency for linear pushbroom images. To solve this problem, this letter presents a novel orthorectification method based on an improved back-projection algorithm. The back-projection algorithm is based on the geometric constraints of the central perspective plane (CPP) and is further improved to process linear pushbroom images with distortions. Specifically, we segment the linear array in the focal plane into multiple line segments and find the exact CPP using simple analytical geometric calculations. The proposed method was fully tested and evaluated with well-known planetary mapping software packages, namely, the integrated system for imagers and spectrometers (ISIS). The experimental results demonstrated that compared with ISIS, the proposed orthorectification method can increase the computational efficiency by more than fivefold and deliver consistent geometric accuracy. The proposed orthorectification method greatly enhances geometric processing capabilities for massive planetary remote sensing images.

Non-negative matrix factorization (NMF) learns to approximate a non-negative matrix by the product of two lower-rank non-negative matrices. Since NMF usually learns sparse representation,it has been widely used in pattern recognition and data mining. However, NMF cannot deal with the datasets that contain offsets. To remedy this problem, Laurberg and Hansen proposed affine NMF (ANMF) by jointly learning the offset vector, but the proposed multiplicative update rule neither guarantees non-negativity constraints over factor matrices nor converges sufficiently rapid. In this paper, we adopt the well-known hierarchical alternating least squares (HALS) algorithm to solve ANMF. Since the update of offset vector is in the same frame of updates of factor matrices, HALS is quite suitable for solving ANMF and the experimental results on simulated datasets validate its efficiency.