No abstract

Semantic segmentation of remote sensing imagery (RSI) has obtained great success with the development of deep convolutional neural networks (DCNNs). However, most of the existing algorithms focus on designing end-to-end DCNNs, but neglecting to consider the difficulty of segmentation in imbalance categories, especially for minority categories in RSI, which limits the performance of RSI semantic segmentation. In this paper, a novel edge guided context aggregation network (EGCAN) is proposed for the semantic segmentation of RSI. The Unet is employed as backbone. Meanwhile, an edge guided context aggregation branch and minority categories extraction branch are designed for a comprehensive enhancement of semantic modeling. Specifically, the edge guided context aggregation branch is proposed to promote entire semantic comprehension of RSI and further emphasize the representation of edge information, which consists of three modules: edge extraction module (EEM), dual expectation maximization attention module (DEMA), and edge guided module (EGM). EEM is created primarily for accurate edge tracking. According to that, DEMA aggregates global contextual features with different scales and the edge features along spatial and channel dimensions. Subsequently, EGM cascades the aggregated features into the decoder process to capture long-range dependencies and further emphasize the error-prone pixels in the edge region to acquire better semantic labels. Besides this, the exploited minority categories extraction branch is presented to acquire rich multi-scale contextual information through an elaborate hybrid spatial pyramid pooling module (HSPP) to distinguish categories taking a small percentage and background. On the Tianzhi Cup dataset, the proposed algorithm EGCAN achieved an overall accuracy of 84.1% and an average cross-merge ratio of 68.1%, with an accuracy improvement of 0.4% and 1.3% respectively compared to the classical Deeplabv3+ model. Extensive experimental results on the dataset released in ISPRS Vaihingen and Potsdam benchmarks also demonstrate the effectiveness of the proposed EGCAN over other state-of-the-art approaches.

No abstract

Object detection is a critical and demanding topic in the subject of processing satellite and airborne images. The targets acquired in remote sensing imagery are at various sizes, and the backgrounds are complicated, which makes object detection extremely challenging. We address these aforementioned issues in this paper by introducing the MashFormer, an innovative multi-scale aware CNN and Transformer integrated hybrid detector. Specifically, MashFormer employs the transformer block to complement the convolutional neural network (CNN) based feature extraction backbone, which could obtain the relationships between long-range features and enhance the representative ability in complex background scenarios. With the intention of improving the detection performance for objects with multi-scale characteristic, since in remote sensing scenarios, the size of object varies greatly. A multi-level feature aggregation component, incoperate with a cross-level feature alignment module is designed to alleviate the semantic discrepancy between features from shallow and deep layers. To verify the effectiveness of the suggested MashFormer, comparative experiments are carried out with other cutting-edge methodologies using the publicly available High Resolution Remote Sensing Detection (HRRSD) and Northwestern Polytechnical University (NWPU) VHR-10 datasets. The experimental findings confirm the effectiveness and superiority of our suggested model by indicating that our approach has greater mean average precision (mAP) than the other methodologies.

Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data. In particular, its capability has expanded from unmixing data samples and detecting targets at the subpixel scale to finding endmembers, which generally cannot be resolved by multispectral imaging. Accordingly, a wealth of new HSI research has been conducted and reported in the literature in recent years. The aim of this Special Issue “Advances in Hyperspectral Data Exploitation“ is to provide a forum for scholars and researchers to publish and share their research ideas and findings to facilitate the utility of hyperspectral imaging in data exploitation and other applications. With this in mind, this Special Issue accepted and published 19 papers in various areas, which can be organized into 9 categories, including I: Hyperspectral Image Classification, II: Hyperspectral Target Detection, III: Hyperspectral and Multispectral Fusion, IV: Mid-wave Infrared Hyperspectral Imaging, V: Hyperspectral Unmixing, VI: Hyperspectral Sensor Hardware Design, VII: Hyperspectral Reconstruction, VIII: Hyperspectral Visualization, and IX: Applications.