Attention-based Sparse and Collaborative Spectral Abundance Learning for Hyperspectral Subpixel Target Detection.

The subpixel target detection in hyperspectral image processing persists as a formidable challenge. In this paper, we present a novel subpixel target detector termed attention-based sparse and collaborative spectral abundance learning for subpixel target detection in hyperspectral images. To help suppress background during subpixel target detection, the proposed method presents a pixel attention-based background sample selection method for background dictionary construction. Besides, the proposed method integrates a band attention-based spectral abundance learning model, replete with sparse and collaborative constraints, in which the band attention map can contribute to enhancing the discriminative ability of the detector in identifying targets from backgrounds. Ultimately, the detection result of the proposed detector is achieved by the learned target spectral abundance after solving the designed model using the alternating direction method of multipliers algorithm. Rigorous experiments conducted on four benchmark datasets, including one simulated and three real-world datasets, validate the effectiveness of the detector with the probability of detection of 90.88%, 96.86%, and 97.79% on the PHI, RIT Campus, and Reno Urban data, respectively, under fixed false alarm rate equal 0.01, indicating that the proposed method yields superior hyperspectral subpixel detection performance and outperforms existing methodologies.

SAAN: Similarity-Aware Attention Flow Network for Change Detection With VHR Remote Sensing Images.

Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. Existing deep learning-based CD methods typically extract bi-temporal image features using a weight-sharing Siamese encoder network and identify change regions using a decoder network. These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions; and 2) the models' confidence in the change regions is inconsistent at different decoder stages. The first problem is because deep encoder layers cannot effectively learn from imbalanced change categories using the sole output supervision, while the second problem is attributed to the lack of explicit semantic consistency preservation. To address these issues, we design a novel similarity-aware attention flow network (SAAN). SAAN incorporates a similarity-guided attention flow module with deeply supervised similarity optimization to achieve effective change detection. Specifically, we counter the first issue by explicitly guiding deep encoder layers to discover semantic relations from bi-temporal input images using deeply supervised similarity optimization. The extracted features are optimized to be semantically similar in the unchanged regions and dissimilar in the changing regions. The second drawback can be alleviated by the proposed similarity-guided attention flow module, which incorporates similarity-guided attention modules and attention flow mechanisms to guide the model to focus on discriminative channels and regions. We evaluated the effectiveness and generalization ability of the proposed method by conducting experiments on a wide range of CD tasks. The experimental results demonstrate that our method achieves excellent performance on several CD tasks, with discriminative features and semantic consistency preserved.

Adversarial pair-wise distribution matching for remote sensing image cross-scene classification.

Remarkable achievements have been made in the field of remote sensing cross-scene classification in recent years. However, most methods directly align the entire image features for cross-scene knowledge transfer. They usually ignore the high background complexity and low category consistency of remote sensing images, which can significantly impair the performance of distribution alignment. Besides, shortcomings of the adversarial training paradigm and the inability to guarantee the prediction discriminability and diversity can also hinder cross-scene classification performance. To alleviate the above problems, we propose a novel cross-scene classification framework in a discriminator-free adversarial paradigm, called Adversarial Pair-wise Distribution Matching (APDM), to avoid irrelevant knowledge transfer and enable effective cross-domain modeling. Specifically, we propose the pair-wise cosine discrepancy for both inter-domain and intra-domain prediction measurements to fully leverage the prediction information, which can suppress negative semantic features and implicitly align the cross-scene distributions. Nuclear-norm maximization and minimization are introduced to enhance the target prediction quality and increase the applicability of the source knowledge, respectively. As a general cross-scene framework, APDM can be easily embedded with existing methods to boost the performance. Experimental results and analyses demonstrate that APDM can achieve competitive and effective performance on cross-scene classification tasks.

Water Impalement Resistance and Drag Reduction of the Superhydrophobic Surface with Hydrophilic Strips.

Superhydrophobic surfaces (SHS) offer versatile applications by trapping an air layer within microstructures, while water jet impact can destabilize this air layer and deactivate the functions of the SHS. The current work presents for the first time that introducing parallel hydrophilic strips to SHS (SHS-s) can simultaneously improve both water impalement resistance and drag reduction (DR). Compared with SHS, SHS-s demonstrates a 125% increase in the enduring time against the impact of water jet with velocity of 11.9 m/s and a 97% improvement in DR at a Reynolds number of 1.4 × 104. The key mechanism lies in the enhanced stability of the air layer due to air confinement by the adjacent three-phase contact lines. These lines not only impede air drainage through the surface microstructures during water jet impact, entrapping the air layer to resist water impalement, but also prevent air floating up due to buoyancy in Taylor-Couette flow, ensuring an even spread of the air layer all over the rotor, boosting DR. Moreover, failure modes of SHS under water jet impact are revealed to be related to air layer decay and surface structure destruction. This mass-producible structured surface holds the potential for widespread use in DR for hulls, autonomous underwater vehicles, and submarines.

TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution.

Transformer-based method has demonstrated promising performance in image super-resolution tasks, due to its long-range and global aggregation capability. However, the existing Transformer brings two critical challenges for applying it in large-area earth observation scenes: (1) redundant token representation due to most irrelevant tokens; (2) single-scale representation which ignores scale correlation modeling of similar ground observation targets. To this end, this paper proposes to adaptively eliminate the interference of irreverent tokens for a more compact self-attention calculation. Specifically, we devise a Residual Token Selective Group (RTSG) to grasp the most crucial token by dynamically selecting the top- k keys in terms of score ranking for each query. For better feature aggregation, a Multi-scale Feed-forward Layer (MFL) is developed to generate an enriched representation of multi-scale feature mixtures during feed-forward process. Moreover, we also proposed a Global Context Attention (GCA) to fully explore the most informative components, thus introducing more inductive bias to the RTSG for an accurate reconstruction. In particular, multiple cascaded RTSGs form our final Top- k Token Selective Transformer (TTST) to achieve progressive representation. Extensive experiments on simulated and real-world remote sensing datasets demonstrate our TTST could perform favorably against state-of-the-art CNN-based and Transformer-based methods, both qualitatively and quantitatively. In brief, TTST outperforms the state-of-the-art approach (HAT-L) in terms of PSNR by 0.14 dB on average, but only accounts for 47.26% and 46.97% of its computational cost and parameters. The code and pre-trained TTST will be available at https://github.com/XY-boy/TTST for validation.

ITER: Image-to-Pixel Representation for Weakly Supervised HSI Classification.

Recent years have witnessed the superiority of deep learning-based algorithms in the field of HSI classification. However, a prerequisite for the favorable performance of these methods is a large number of refined pixel-level annotations. Due to atmospheric changes, sensor differences, and complex land cover distribution, pixel-level labeling of high-dimensional hyperspectral image (HSI) is extremely difficult, time-consuming, and laborious. To overcome the above hurdle, an Image-To-pixEl Representation (ITER) approach is proposed in this paper. To the best of our knowledge, this is the first time that image-level annotation is introduced to predict pixel-level classification maps for HSI. The proposed model is along the lines of subject modeling to boundary refinement, corresponding to pseudo-label generation and pixel-level prediction. Concretely, in the pseudo-label generation part, the spectral/spatial activation, spectral-spatial alignment loss, and geographic element enhancement are sequentially designed to locate discriminate regions of each category, optimize multi-domain class activation map (CAM) collaborative training, and refine labels, respectively. For the pixel-level prediction portion, a high frequency-aware self-attention in a high-enhanced transformer is put forward to achieve detailed feature representation. With the two-stage pipeline, ITER explores weakly supervised HSI classification with image-level tags, bridging the gap between image-level annotation and dense prediction. Extensive experiments in three benchmark datasets with state-of-the-art (SOTA) works show the performance of the proposed approach.

Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning.

This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT) field representation that encodes line segments using a closed-form 4D geometric vector field. The proposed HAWP consists of three sequential components empowered by end-to-end and HAT-driven designs: 1) generating a dense set of line segments from HAT fields and endpoint proposals from heatmaps, 2) binding the dense line segments to sparse endpoint proposals to produce initial wireframes, and 3) filtering false positive proposals through a novel endpoint-decoupled line-of-interest aligning (EPD LOIAlign) module that captures the co-occurrence between endpoint proposals and HAT fields for better verification. Thanks to our novel designs, HAWPv2 shows strong performance in fully supervised learning, while HAWPv3 excels in self-supervised learning, achieving superior repeatability scores and efficient training (24 GPU hours on a single GPU). Furthermore, HAWPv3 exhibits a promising potential for wireframe parsing in out-of-distribution images without providing ground truth labels of wireframes.

Explicable Fine-Grained Aircraft Recognition Via Deep Part Parsing Prior Framework for High-Resolution Remote Sensing Imagery.

Aircraft recognition is crucial in both civil and military fields, and high-spatial resolution remote sensing has emerged as a practical approach. However, existing data-driven methods fail to locate discriminative regions for effective feature extraction due to limited training data, leading to poor recognition performance. To address this issue, we propose a knowledge-driven deep learning method called the explicable aircraft recognition framework based on a part parsing prior (APPEAR). APPEAR explicitly models the aircraft's rigid structure as a pixel-level part parsing prior, dividing it into five parts: 1) the nose; 2) left wing; 3) right wing; 4) fuselage; and 5) tail. This fine-grained prior provides reliable part locations to delineate aircraft architecture and imposes spatial constraints among the parts, effectively reducing the search space for model optimization and identifying subtle interclass differences. A knowledge-driven aircraft part attention (KAPA) module uses this prior to achieving a geometric-invariant representation for identifying discriminative features. Part features are generated by part indexing in a specific order and sequentially embedded into a compact space to obtain a fixed-length representation for each part, invariant to aircraft orientation and scale. The part attention module then takes the embedded part features, adaptively reweights their importance to identify discriminative parts, and aggregates them for recognition. The proposed APPEAR framework is evaluated on two aircraft recognition datasets and achieves superior performance. Moreover, experiments with few-shot learning methods demonstrate the robustness of our framework in different tasks. Ablation analysis illustrates that the fuselage and wings of the aircraft are the most effective parts for recognition.

FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery.

Geospatial object segmentation, a fundamental Earth vision task, always suffers from scale variation, the larger intra-class variance of background, and foreground-background imbalance in high spatial resolution (HSR) remote sensing imagery. Generic semantic segmentation methods mainly focus on the scale variation in natural scenarios. However, the other two problems are insufficiently considered in large area Earth observation scenarios. In this paper, we propose a foreground-aware relation network (FarSeg++) from the perspectives of relation-based, optimization-based, and objectness-based foreground modeling, alleviating the above two problems. From the perspective of the relations, the foreground-scene relation module improves the discrimination of the foreground features via the foreground-correlated contexts associated with the object-scene relation. From the perspective of optimization, foreground-aware optimization is proposed to focus on foreground examples and hard examples of the background during training to achieve a balanced optimization. Besides, from the perspective of objectness, a foreground-aware decoder is proposed to improve the objectness representation, alleviating the objectness prediction problem that is the main bottleneck revealed by an empirical upper bound analysis. We also introduce a new large-scale high-resolution urban vehicle segmentation dataset to verify the effectiveness of the proposed method and push the development of objectness prediction further forward. The experimental results suggest that FarSeg++ is superior to the state-of-the-art generic semantic segmentation methods and can achieve a better trade-off between speed and accuracy.

Overcoming the Barrier of Incompleteness: A Hyperspectral Image Classification Full Model.

Deep learning-based methods have shown promising outcomes in many fields. However, the performance gain is always limited to a large extent in classifying hyperspectral image (HSI). We discover that the reason behind this phenomenon lies in the incomplete classification of HSI, i.e., existing works only focus on a certain stage that contributes to the classification, while ignoring other equally or even more significant phases. To address the above issue, we creatively put forward three elements needed for complete classification: the extensive exploration of available features, adequate reuse of representative features, and differential fusion of multidomain features. To the best of our knowledge, these three elements are being established for the first time, providing a fresh perspective on designing HSI-tailored models. On this basis, an HSI classification full model (HSIC-FM) is proposed to overcome the barrier of incompleteness. Specifically, a recurrent transformer corresponding to Element 1 is presented to comprehensively extract short-term details and long-term semantics for local-to-global geographical representation. Afterward, a feature reuse strategy matching Element 2 is designed to sufficiently recycle valuable information aimed at refined classification using few annotations. Eventually, a discriminant optimization is formulized in accordance with Element 3 to distinctly integrate multidomain features for the purpose of constraining the contribution of different domains. Numerous experiments on four datasets at small-, medium-, and large-scale demonstrate that the proposed method outperforms the state-of-the-art (SOTA) methods, such as convolutional neural network (CNN)-, fully convolutional network (FCN)-, recurrent neural network (RNN)-, graph convolutional network (GCN)-, and transformer-based models (e.g., accuracy improvement of more than 9% with only five training samples per class). The code will be available soon at https://github.com/jqyang22/ HSIC-FM.

HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search.

Recent neural architecture search (NAS)-based approaches have made great progress in the hyperspectral image (HSI) classification tasks. However, the architectures are usually optimized independently of the network weights, increasing searching time, and restricting model performances. To tackle these issues, in this article, different from previous methods that extra define structural parameters, we propose to directly generate structural parameters by utilizing the specifically designed hyper kernels, ingeniously converting the original complex dual optimization problem into easily implemented one-tier optimizations, and greatly shrinking searching costs. Then, we develop a hierarchical multimodule search space whose candidate operations only contain convolutions, and these operations can be integrated into unified kernels. Using the above searching strategy and searching space, we obtain three kinds of networks to separately conduct pixel-level or image-level classifications with 1-D or 3-D convolutions. In addition, by combining the proposed hyper kernel searching scheme with the 3-D convolution decomposition mechanism, we obtain diverse architectures to simulate 3-D convolutions, greatly improving network flexibilities. A series of quantitative and qualitative experiments on six public datasets demonstrate that the proposed methods achieve state-of-the-art results compared with other advanced NAS-based HSI classification approaches.

Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification.

Convolutional neural networks (CNNs) have been widely applied to hyperspectral image classification (HSIC). However, traditional convolutions can not effectively extract features for objects with irregular distributions. Recent methods attempt to address this issue by performing graph convolutions on spatial topologies, but fixed graph structures and local perceptions limit their performances. To tackle these problems, in this article, different from previous approaches, we perform the superpixel generation on intermediate features during network training to adaptively produce homogeneous regions, obtain graph structures, and further generate spatial descriptors, which are served as graph nodes. Besides spatial objects, we also explore the graph relationships between channels by reasonably aggregating channels to generate spectral descriptors. The adjacent matrices in these graph convolutions are obtained by considering the relationships among all descriptors to realize global perceptions. By combining the extracted spatial and spectral graph features, we finally obtain a spectral-spatial graph reasoning network (SSGRN). The spatial and spectral parts of SSGRN are separately called spatial and spectral graph reasoning subnetworks. Comprehensive experiments on four public datasets demonstrate the competitiveness of the proposed methods compared with other state-of-the-art graph convolution-based approaches.

DCN-T: Dual Context Network With Transformer for Hyperspectral Image Classification.

Hyperspectral image (HSI) classification is challenging due to spatial variability caused by complex imaging conditions. Prior methods suffer from limited representation ability, as they train specially designed networks from scratch on limited annotated data. We propose a tri-spectral image generation pipeline that transforms HSI into high-quality tri-spectral images, enabling the use of off-the-shelf ImageNet pretrained backbone networks for feature extraction. Motivated by the observation that there are many homogeneous areas with distinguished semantic and geometric properties in HSIs, which can be used to extract useful contexts, we propose an end-to-end segmentation network named DCN-T. It adopts transformers to effectively encode regional adaptation and global aggregation spatial contexts within and between the homogeneous areas discovered by similarity-based clustering. To fully exploit the rich spectrums of the HSI, we adopt an ensemble approach where all segmentation results of the tri-spectral images are integrated into the final prediction through a voting scheme. Extensive experiments on three public benchmarks show that our proposed method outperforms state-of-the-art methods for HSI classification. The code will be released at https://github.com/DotWang/DCN-T.

Can Spectral Information Work While Extracting Spatial Distribution?-An Online Spectral Information Compensation Network for HSI Classification.

In the past few years, deep learning-based methods have shown commendable performance for hyperspectral image (HSI) classification. Many works focus on designing independent spectral and spatial branches and then fusing the output features from two branches for category prediction. In this way, the correlation that exists between spectral and spatial information is not completely explored, and spectral information extracted from one branch is always not sufficient. Some studies also try to directly extract spectral-spatial features using 3D convolutions but are accompanied by the severe over-smoothing phenomenon and poor representation ability of spectral signatures. Unlike the above-mentioned approaches, in this paper, we propose a novel online spectral information compensation network (OSICN) for HSI classification, which consists of a candidate spectral vector mechanism, progressive filling process, and multi-branch network. To the best of our knowledge, this paper is the first to online supplement spectral information into the network when spatial features are extracted. The proposed OSICN makes the spectral information participate in network learning in advance to guide spatial information extraction, which truly processes spectral and spatial features in HSI as a whole. Accordingly, OSICN is more reasonable and more effective for complex HSI data. Experimental results on three benchmark datasets demonstrate that the proposed approach has more outstanding classification performance compared with the state-of-the-art methods, even with a limited number of training samples.

Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection.

Deep learning for change detection is one of the current hot topics in the field of remote sensing. However, most end-to-end networks are proposed for supervised change detection, and unsupervised change detection models depend on traditional pre-detection methods. Therefore, we proposed a fully convolutional change detection framework with generative adversarial network, to unify unsupervised, weakly supervised, regional supervised, and fully supervised change detection tasks into one end-to-end framework. A basic Unet segmentor is used to obtain change detection map, an image-to-image generator is implemented to model the spectral and spatial variation between multi-temporal images, and a discriminator for changed and unchanged is proposed for modeling the semantic changes in weakly and regional supervised change detection task. The iterative optimization of segmentor and generator can build an end-to-end network for unsupervised change detection, the adversarial process between segmentor and discriminator can provide the solutions for weakly and regional supervised change detection, the segmentor itself can be trained for fully supervised task. The experiments indicate the effectiveness of the propsed framework in unsupervised, weakly supervised and regional supervised change detection. This article provides new theorical definitions for unsupervised, weakly supervised and regional supervised change detection tasks with the proposed framework, and shows great potentials in exploring end-to-end network for remote sensing change detection (https://github.com/Cwuwhu/FCD-GAN-pytorch).

Learning Single Spectral Abundance for Hyperspectral Subpixel Target Detection.

Due to the limitation of target size and spatial resolution, targets of interest in hyperspectral images (HSIs) often appear as subpixel targets, which makes hyperspectral target detection still faces an important bottleneck, that is, subpixel target detection. In this article, we propose a new detector by learning single spectral abundance for hyperspectral subpixel target detection (denoted as LSSA). Different from most existing hyperspectral detectors that are designed based on a match of the spectrum assisted by spatial information or focusing on the background, the proposed LSSA addresses the problem of detecting subpixel targets by learning a spectral abundance of the target of interest directly. In LSSA, the abundance of the prior target spectrum is updated and learned, while the prior target spectrum is fixed in a nonnegative matrix factorization (NMF) model. It turns out that such a way is quite effective to learn the abundance of subpixel targets and contributes to detecting subpixel targets in hyperspectral imagery (HSI). Numerous experiments are conducted on one simulated dataset and five real datasets, and the results indicate that the LSSA yields superior performance in hyperspectral subpixel target detection and outperforms its counterparts.

Collaborative-guided spectral abundance learning with bilinear mixing model for hyperspectral subpixel target detection.

Detecting subpixel targets is a considerably challenging issue in hyperspectral image processing and interpretation. Most of the existing hyperspectral subpixel target detection methods construct detectors based on the linear mixing model which regards a pixel as a linear combination of different spectral signatures. However, due to the multiple scattering, the linear mixing model cannot​ illustrate the multiple materials interactions that are nonlinear and widespread in real-world hyperspectral images, which could result in unsatisfactory performance in detecting subpixel targets. To alleviate this problem, this work presents a novel collaborative-guided spectral abundance learning model (denoted as CGSAL) for subpixel target detection based on the bilinear mixing model in hyperspectral images. The proposed CGSAL detects subpixel targets by learning a spectral abundance of the target signature in each pixel. In CGSAL, virtual endmembers and their abundance help to achieve good accuracy for modeling nonlinear scattering accounts for multiple materials interactions according to the bilinear mixing model. Besides, we impose a collaborative term to the spectral abundance learning model to emphasize the collaborative relationships between different endmembers, which contributes to accurate spectral abundance learning and further help to detect subpixel targets. Plentiful experiments and analyses are conducted on three real-world and one synthetic hyperspectral datasets to evaluate the effectiveness of the CGSAL in subpixel target detection. The experiment results demonstrate that the CGSAL achieves competitive performance in detecting subpixel targets and outperforms other state-of-the-art hyperspectral subpixel target detectors.

Binary Change Guided Hyperspectral Multiclass Change Detection.

Characterized by tremendous spectral information, hyperspectral image is able to detect subtle changes and discriminate various change classes for change detection. The recent research works dominated by hyperspectral binary change detection, however, cannot provide fine change classes information. And most methods incorporating spectral unmixing for hyperspectral multiclass change detection (HMCD), yet suffer from the neglection of temporal correlation and error accumulation. In this study, we proposed an unsupervised Binary Change Guided hyperspectral multiclass change detection Network (BCG-Net) for HMCD, which aims at boosting the multiclass change detection result and unmixing result with the mature binary change detection approaches. In BCG-Net, a novel partial-siamese united-unmixing module is designed for multi-temporal spectral unmixing, and a groundbreaking temporal correlation constraint directed by the pseudo-labels of binary change detection result is developed to guide the unmixing process from the perspective of change detection, encouraging the abundance of the unchanged pixels more coherent and that of the changed pixels more accurate. Moreover, an innovative binary change detection rule is put forward to deal with the problem that traditional rule is susceptible to numerical values. The iterative optimization of the spectral unmixing process and the change detection process is proposed to eliminate the accumulated errors and bias from unmixing result to change detection result. The experimental results demonstrate that our proposed BCG-Net could achieve comparative or even outstanding performance of multiclass change detection among the state-of-the-art approaches and gain better spectral unmixing results at the same time.

ESCNet: An End-to-End Superpixel-Enhanced Change Detection Network for Very-High-Resolution Remote Sensing Images.

Change detection (CD), as one of the central problems in Earth observation, has attracted a lot of research interest over recent decades. Due to the rapid development of satellite sensors in recent years, we have witnessed an enrichment of the CD source data with the availability of very-high-resolution (VHR) multispectral imagery, which provides abundant change clues. However, precisely locating real changed areas still remains a challenge. In this article, we propose an end-to-end superpixel-enhanced CD network (ESCNet) for VHR images, which combines differentiable superpixel segmentation and a deep convolutional neural network (DCNN). Two weight-sharing superpixel sampling networks (SSNs) are tailored for the feature extraction and superpixel segmentation of bitemporal image pairs. A UNet-based Siamese neural network is then employed to mine the different information. The superpixels are then leveraged to reduce the latent noise in the pixel-level feature maps while preserving the edges, where a novel superpixelation module is used to serve this purpose. Furthermore, to compensate for the dependence on the number of superpixels, we propose an innovative adaptive superpixel merging (ASM) module, which has a concise form and is fully differentiable. A pixel-level refinement module making use of the multilevel decoded features is also appended to the end of the framework. Experiments on two public datasets confirmed the superiority of ESCNet compared to the traditional and state-of-the-art (SOTA) deep learning-based CD (DLCD) methods.

An Accurate UAV 3-D Path Planning Method for Disaster Emergency Response Based on an Improved Multiobjective Swarm Intelligence Algorithm.

Planning a practical three-dimensional (3-D) flight path for unmanned aerial vehicles (UAVs) is a key challenge for the follow-up management and decision making in disaster emergency response. The ideal flight path is expected to balance the total flight path length and the terrain threat, to shorten the flight time and reduce the possibility of collision. However, in the traditional methods, the tradeoff between these concerns is difficult to achieve, and practical constraints are lacking in the optimized objective functions, which leads to inaccurate modeling. In addition, the traditional methods based on gradient optimization lack an accurate optimization capability in the complex multimodal objective space, resulting in a nonoptimal path. Thus, in this article, an accurate UAV 3-D path planning approach in accordance with an enhanced multiobjective swarm intelligence algorithm is proposed (APPMS). In the APPMS method, the path planning mission is converted into a multiobjective optimization task with multiple constraints, and the objectives based on the total flight path length and degree of terrain threat are simultaneously optimized. In addition, to obtain the optimal UAV 3-D flight path, an accurate swarm intelligence search approach based on improved ant colony optimization is introduced, which can improve the global and local search capabilities by using the preferred search direction and random neighborhood search mechanism. The effectiveness of the proposed APPMS method was demonstrated in three groups of simulated experiments with different degrees of terrain threat, and a real-data experiment with 3-D terrain data from an actual emergency situation.