Due to advances in remote sensing satellite imaging and image processing technologies and their wide applications, intelligent remote sensing satellites are facing an opportunity for rapid development. The key technologies, standards, and laws of intelligent remote sensing satellites are also experiencing a series of new challenges. Novel concepts and key technologies in the intelligent hyperspectral remote sensing satellite system have been proposed since 2011. The aim of these intelligent remote sensing satellites is to provide real-time, accurate, and personalized remote sensing information services. This article reviews the current developments in new-generation intelligent remote sensing satellite systems, with a focus on intelligent remote sensing satellite platforms, imaging payloads, onboard processing systems, and other key technological chains. The technological breakthroughs and current defects of intelligence-oriented designs are also analyzed. Intelligent remote sensing satellites collect personalized remote sensing data and information, with real-time data features and information interaction between remote sensing satellites or between satellites and the ground. Such developments will expand the use of remote sensing applications beyond government departments and industrial users to a massive number of individual users. However, this extension faces challenges regarding privacy protection, societal values, and laws regarding the sharing and distribution of data and information.

Object detection is a focal point in remote sensing applications. Remote sensing images typically contain a large number of small objects and a wide range of orientations across objects. This results in great challenges to small object detection approaches based on remote sensing images. Methods directly employ channel relations with equal weights to construct information features leads to inadequate feature representation in complex image small object detection tasks. Multiscale detection methods improve the speed and accuracy of detection, while small objects themselves contain limited information, and the features are easily lost following down-sampling. During the detection, the feature images are independent across scales, resulting in a discontinuity at the detection scale. In this article, we propose the multiscale context and enhanced channel attention (MSCCA) model. MSCCA employs PeleeNet as the backbone network. In particular, the feature image channel attention is enhanced and the multiscale context information is fused with multiscale detection methods to improve the characterization ability of the convolutional neural network. The proposed MSCCA method is evaluated on two real datasets. Results show that for 512 × 512 input images, MSCCA was able to achieve 80.4% and 94.4% mAP on the DOTA and NWPU VHR-10, respectively. Meanwhile, the model size of MSCCA is 21% smaller than that of its predecessor. MSCCA can be considered as a practical lightweight oriented object detection model in remote sensing images.

Target detection is a critical task in interpreting aerial images. Small target detection, such as vehicles, is challenging. Different lighting conditions affect the accuracy of vehicle detection. For example, vehicles are difficult to distinguish from the background in RGB images under low illumination conditions. In contrast, under high illumination conditions, the color and texture of vehicles are not significantly different in thermal infrared (TIR) images. To improve the accuracy of vehicle detection under various illumination conditions, we propose an adaptive multi-modal feature fusion and cross-modal vehicle index (AFFCM) model for vehicle detection. Based on the single-stage object detection model, AFFCM uses red, green, blue, and thermal infrared (RGB-T) images. It comprises three parts: 1) the softpooling channel attention (SCA) mechanism calculates the cross-modal feature weights of the RGB and TIR features using a fully connected layer during global weighted pooling. 2) We design a multi-modal adaptive feature fusion (MAFF) module based on the cross-modal feature weights derived from the SCA mechanism. The MAFF selects features with high weight, compresses redundant features with low weight, and performs adaptive fusion using a multi-scale feature pyramid. 3) A cross-modal vehicle index is established to extract the target area, suppress complex background information, and minimize false alarms in vehicle detection. The mean average precision (mAP) on the Drone Vehicle dataset are 14.44% and 5.02% higher than those obtained using only RGB or TIR images. The mAP is 2.63% higher than that of state-of-the-art (SOTA) methods that utilize RGB and TIR images.

Onboard processing systems are becoming very important in remote sensing data processing. However, a main problem with specialized hardware architectures used for onboard processing is their high power consumption, which limits their exploitation in earth observation missions. In this paper, a novel strategy for approximate computing is proposed for reducing energy consumption in remotely sensed onboard processing tasks. As a case study, the implementation of support vector machine (SVM) hyperspectral image classification is considered by using the proposed approximate computing framework. Experimental results show that the proposed approximate computing scheme achieves up to 70% power savings in the kernel accumulation computation procedure with negligible degradation of classification accuracy as compared to the traditional ripple carry adder (RCA) precise computation. This is an important achievement to meet the restrictions of onboard processing scenarios.

Object detection based on remote sensing imagery has become increasingly popular over the past few years. Unlike natural images taken by humans or surveillance cameras, the scale of remote sensing images is large, which requires the training and inference procedure to be on a cutting image. However, objects appearing in remote sensing imagery are often sparsely distributed and the labels for each class are imbalanced. This results in unstable training and inference. In this paper, we analyze the training characteristics of the remote sensing images and propose the fusion of the aggregated-mosaic training method, with the assigned-stitch augmentation and auto-target-duplication. In particular, based on the ground truth and mosaic image size, the assigned-stitch augmentation enhances each training sample with an appropriate account of objects, facilitating the smooth training procedure. Hard to detect objects, or those in classes with rare samples, are randomly selected and duplicated by the auto-target-duplication, which solves the sample imbalance or classes with insufficient results. Thus, the training process is able to focus on weak classes. We employ VEDAI and NWPU VHR-10, remote sensing datasets with sparse objects, to verify the proposed method. The YOLOv5 adopts the Mosaic as the augmentation method and is one of state-of-the-art detectors, so we choose Mosaic (YOLOv5) as the baseline. Results demonstrate that our method outperforms Mosaic (YOLOv5) by 2.72% and 5.44% on 512 × 512 and 1024 × 1024 resolution imagery, respectively. Moreover, the proposed method outperforms Mosaic (YOLOv5) by 5.48% under the NWPU VHR-10 dataset.

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.