Object detection in high spatial resolution remote sensing images (HSRIs) is an important part of image information automatic extraction, analysis, and understanding. The region of interest (ROI) scale of object detection and the object feature representation are two vital factors in HSRI object detection. With respect to these two issues, this article presents a novel HSRI object detection method based on convolutional neural networks (CNNs) with suitable object scale features. First, the suitable ROI scale of object detection is obtained by compiling statistics for the scale range of objects in HSRIs. Then, a CNN framework for object detection in HSRIs is designed using a suitable ROI scale of object detection. The object features obtained using a CNN have good universality and robustness. Finally, a CNN framework with a suitable ROI scale of object detection is trained and tested. Using the WHU-RSONE data set, the proposed method is compared with the faster region-based CNN (Faster-RCNN) framework. The experimental results show that the proposed method outperforms the Faster-RCNN framework and provides good object detection results in HSRIs.

Satellite jitter (SJ) is an important error source that affects the geometric accuracy of high resolution satellite imagery. In this paper, the quantitative relationship between the jitter displacement (image displacement caused by SJ) and relative registration error obtained from parallax images is deduced to be theoretical in detail, and the jitter displacement estimation model is built to estimate the jitter displacement. Then, a simulation experiment is carried out to validate the feasibility of using the built jitter displacement estimation model to estimate the jitter displacement. Finally, experiments with real images in DengFeng (China) including multispectral images of Ziyuan-3 (ZY-3) satellite and high resolution (HR) images of Ziyuan1-02C (ZY1-02C) satellite are used to validate the effectiveness of utilizing the built jitter displacement estimation model to do the jitter estimation. High accuracy ground reference data are further used to evaluate the accuracy of the estimation. Experimental results show that the average estimated error for jitter displacement of ZY-3 is 2.96% and 0.11% in amplitude and frequency respectively, and the estimated error for jitter displacement of ZY1-02C is 8.46% and 0.35% in amplitude and frequency, respectively.

Satellite jitter is an important factor that affects the geometric accuracy of high-resolution satellite imagery. However, it is often ignored when there are no high frequency and accurate attitude observations. This letter proposes a jitter detection and compensation method using multispectral imagery, which can improve the band-to-band registration accuracy and eliminate image distortion caused by satellite jitter only using image data. In the method, a jitter detection and compensation model is built to estimate the periodic distortion from band-to-band registration error of multispectral imagery. Both simulation data and Ziyuan-3 (ZY-3) satellite multispectral images were used to validate the presented method. In the experiment of ZY-3, the band-to-band registration accuracy of the compensated image is improved to 0.05 pixels in both across and along track directions and the distortions across and along the track are both supressed to less than 0.2 pixels as determined from the known positions of ground control points (GCPs), showing that the distortion caused by satellite jitter can be accurately estimated and corrected by the proposed method.

The aim of weakly supervised object co-localization is to locate different objects of the same superclass in a dataset. Recent methods achieve impressive co-localization performance by multiple instance learning and self-supervised learning. However, these methods ignore the common part information shared by fine-grained objects and the influence of the complementary parts on the co-localization of the fine-grained objects. To solve these issues, we propose a complementary parts contrastive learning method for fine-grained weakly supervised object co-localization. The proposed method follows such an assumption that fine-grained object parts with the same/different semantic meaning should have similar/dissimilar feature representations in the feature space. The proposed method tackles two critical issues in this task: i ) how to spread the model’s attention and suppress the complex background noise, and ii ) how to leverage the cross-category common parts information to mitigate the context co-occurrence problem. To address i ), we attempt to integrate local and context cues via three types of attention including self-supervised attention, channel, and spatial attention to spread the model’s attention toward automatically identifying and localizing most discriminative parts of objects in the fine-grained images. To solve ii ), we propose a cross-category object complementarity part contrastive learning module to identify the extracted part regions with different semantic information by pulling the same part features closer and pushing different part features away, which can mitigate the confounding bias caused by the co-occurrence surroundings within specific classes. Extensive qualitative and quantitative evaluations demonstrate the effectiveness of the proposed method on four fine-grained co-localization datasets: CUB-200-2011, Stanford Cars, FGVC-Aircraft, and Stanford Dogs. Code and models are available at https://github.com/Zhao-fan/CPCL.

Relative radiometric calibration, or flat fielding, is indispensable for obtaining high-quality optical satellite imagery for sensors that have more than one detector per band. High-resolution optical push-broom sensors with thousands of detectors per band are now common. Multiple techniques have been employed for relative radiometric calibration. One technique, often called side-slither, where the sensor axis is rotated 90° in yaw relative to normal acquisitions, has been gaining popularity, being applied to Landsat 8, QuickBird, RapidEye, and other satellites. Side-slither can be more time efficient than some of the traditional methods, as only one acquisition may be required. In addition, the side-slither does not require any onboard calibration hardware, only a satellite capability to yaw and maintain a stable yawed attitude. A relative radiometric calibration method based on histograms of side-slither data is developed. This method has three steps: pre-processing, extraction of key points, and calculation of coefficients. Histogram matching and Otsu’s method are used to extract key points. Three datasets from the Chinese GaoFen-9 satellite were used: one to obtain the relative radiometric coefficients, and the others to verify the coefficients. Root-mean-square deviations of the corrected imagery were better than 0.1%. The maximum streaking metrics was less than 1. This method produced significantly better relative radiometric calibration than the traditional method used for GaoFen-9.

Gaofen-1 02/03/04 satellites, the first civilian high resolution optical operational constellation in China, have Earth observation capabilities with panchromatic/multispectral imaging at 2/8 m resolution. Satellite jitter, the fluctuation of satellite points, has a negative influence on the geometric quality of high-resolution optical satellite imagery. This paper presents an improved jitter detection method based on parallax observation of multispectral sensors for Gaofen-1 02/03/04 satellites, which can eliminate the effect of the relative internal error induced by lens distortion, and accurately estimate the parameters of satellite jitter. The relative internal error is estimated by polynomial modelling and removed from the original parallax image generated by pixel-to-pixel image matching between two bands of images. The accurate relative time-varying error and absolute distortion caused by satellite jitter could be estimated by using the sine function. Three datasets of multispectral images captured by Gaofen-1 02/03/04 satellites were used to conduct the experiments. The results show that the relative system errors in both the across- and along-track directions can be modelled with a quadratic polynomial, and satellite jitter with a frequency of 1.1–1.2 Hz in the across-track direction was detected for the first time. The amplitude of the jitter differed in the three datasets. The largest amplitude, from satellite 04, is 1.3 pixels. The smallest amplitude, from satellite 02, is 0.077 pixels. The reliability and accuracy of the detection results were verified by using two groups of band combinations and ortho-images with a 1 m resolution. The comparison results show that the detection accuracy is improved by approximately 30% using the proposed method.