This paper presents a novel adaptive fuzzy local information c-means (ADFLICM) clustering approach for remotely sensed imagery classification by incorporating the local spatial and gray level information constraints. The ADFLICM approach can enhance the conventional fuzzy c-means algorithm by producing homogeneous segmentation and reducing the edge blurring artifact simultaneously. The major contribution of ADFLICM is use of the new fuzzy local similarity measure based on pixel spatial attraction model, which adaptively determines the weighting factors for neighboring pixel effects without any experimentally set parameters. The weighting factor for each neighborhood is fully adaptive to the image content, and the balance between insensitiveness to noise and reduction of edge blurring artifact to preserve image details is automatically achieved by using the new fuzzy local similarity measure. Four different types of images were used in the experiments to examine the performance of ADFLICM. The experimental results indicate that ADFLICM produces greater accuracy than the other four methods and hence provides an effective clustering algorithm for classification of remotely sensed imagery.

Landslide inventory is significant for landslide disaster reduction. To construct the landslide inventory, deep learning has received growing attention to detect landslides from satellite images. Among various deep learning algorithms, you-only-look-once (YOLO) has a strong ability to detect objects efficiently and has been widely used in landslide extraction. Despite its efficiency, there is no general rule to select the backbone and attention mechanism for YOLO. The selection of these two modules depends on specific application needs. Meanwhile, YOLO output is a series of anchor boxes, not accurate landslide boundaries. A single bounding box may contain many landslides and cannot extract individual landslides, limiting the YOLO applications in constructing landslide inventory. To address these issues, this article presents a lightweight attention-guided YOLO with level set layer (LA-YOLO-LLL) for landslide detection from optical satellite images. First, we introduced the MobileNetv3 to replace the original backbone of YOLO to simultaneously reduce the parameter complexity and improve the model transferability. Then, we presented a light pyramid features reuse fusion attention mechanism to improve landslide detection performance. Finally, we integrated the level set layer into YOLO head to produce accurate landslide boundaries. This article validated the accuracy and transferability of the presented method in two study areas (Bijie and Taiwan) with similar geo-environmental conditions. Experimental results show that the presented LA-YOLO-LLL model outperformed traditional YOLO in landslide detection. Findings in this article are valuable for landslide inventory construction, land use planning and risk control.

In this letter, we propose a change detection method based on Gabor wavelet features for very high resolution (VHR) remote sensing images. First, Gabor wavelet features are extracted from two temporal VHR images to obtain spatial and contextual information. Then, the Gabor-wavelet-based difference measure (GWDM) is designed to generate the difference image. In GWDM, a new local similarity measure is defined, in which the Markov random field neighborhood system is incorporated to obtain a local relationship, and the coefficient of variation method is applied to discriminate contributions from different features. Finally, the fuzzy c-means cluster algorithm is employed to obtain the final change map. Experiments employing QuickBird and SPOT5 images demonstrate the effectiveness of the proposed approach.

Spatiotemporal fusion technique can combine the advantages of temporal resolution and spatial resolution of different images to achieve continuous monitoring for the Earth’s surface, which is a feasible solution to resolve the trade-off between the temporal and spatial resolutions of remote sensing images. In this paper, an object-based spatiotemporal fusion model (OBSTFM) is proposed to produce spatiotemporally consistent data, especially in areas experiencing non-shape changes (including phenology changes and land cover changes without shape changes). Considering different changes that might occur in different regions, multi-resolution segmentation is first employed to produce segmented objects, and then a linear injection model is introduced to produce preliminary prediction. In addition, a new optimized strategy to select similar pixels is developed to obtain a more accurate prediction. The performance of proposed OBSTFM is validated using two remotely sensed dataset experiencing phenology changes in the heterogeneous area and land cover type changes, experimental results show that the proposed method is advantageous in such areas with non-shape changes, and has satisfactory robustness and reliability in blending large-scale abrupt land cover changes. Consequently, OBSTFM has great potential for monitoring highly dynamic landscapes.

Urban hotspots are regions with intensive passenger flow, sound infrastructure, and thriving business during a certain period of time, which mirror the travel behavior of residents. Taxi trajectory is one of the important data sources for urban hotspot detection. Unfortunately, it should be pointed out that quite a few of the relevant studies have ignored the temporal dynamics or network-constrained characteristics of urban hotspots, making the detecting results less reasonable and reliable. In this study, a two-phase clustering approach is proposed to detect urban hotspot with taxi trajectory. Concretely, in the first phase, spatiotemporal hierarchical density-based spatial clustering of applications with noise is utilized to cluster the trajectory points with spatial and temporal attributes, which is essential for understanding the evolution of urban hotspots over time. In the second phase, the idea of region growing is introduced to further filter noise, in which the spatial similarity between data points is measured by the route distance, considering that the trajectory data are constrained by the road network. A case study is carried out by the proposed method. Meanwhile, in combination with the Luojia1-01 night-time light remote sensing data and POI data, the reliability of the clustering results is verified and the semantic meaning of the discovered clusters is enriched. Furthermore, not only the spatiotemporal distribution but also the trip lengths and directions of the detected hotspots are explored. These findings can serve as a scientific basis for policymakers in traffic control, public facilities planning, as well as location-based service.

This paper presents a fuzzy local double neighborhood information c-means (FLDNICM) clustering algorithm for remotely sensed imagery classification, which incorporates flexible and accurate local spatial and spectral information. First, a tradeoff weighted fuzzy factor is established based on a pixel spatial attraction model that considers spatial distance and class membership differences between the central pixel and its neighbor simultaneously. This factor can adaptively and accurately estimate the spatial constraints from neighboring pixels. To further enhance robustness to noise and outliers, another fuzzy prior probability function is also defined, which integrates the mutual dependence information from a pixel and its neighbor in a fuzzy logical way for obtaining accurate spatial contextual information. The FLDNICM enhances the conventional fuzzy c-means algorithm by producing homogeneous segmentation while reducing the edge blurring artifacts. The new trade-off weighted fuzzy factor and prior probability function are both parameter free and fully adaptive to the image content. Experimental results demonstrate the superiority of FLDNICM over competing methodologies, considering a series of synthetic and real-world images classification applications.