Shadow often results in difficulties for subsequent image applications of multispectral satellite remote sensing images, like object recognition and change detection. With continuous improvement in both spatial and spectral resolutions of satellite remote sensing images, a more serious impact occurs on satellite remote sensing image interpretation due to the existence of shadow. Though various shadow detection methods have been developed, problems of both shadow omission and nonshadow misclassification still exist for detecting shadow well in high-resolution multispectral satellite remote sensing images. These shadow detection problems mainly include high small shadow omission and typical nonshadow misclassification (like bluish and greenish nonshadow misclassification, and large dark nonshadow misclassification). For further resolving these problems, a new shadow index is developed based on the analysis of the property difference between shadow and the corresponding nonshadow with several multispectral band components (i.e., near-infrared, red, green and blue components) and hue and intensity components in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ), respectively. The shadow mask is further acquired by applying an optimal threshold determined automatically on the shadow index image. The final shadow image is further optimized with a definite morphological operation of opening and closing. The proposed algorithm is verified with many images from WorldView-3 and WorldView-2 acquired at different times and sites. The proposed algorithm performance is particularly evaluated by qualitative visual sense comparison and quantitative assessment of shadow detection results in comparative experiments with two WorldView-3 test images of Tripoli, Libya. Both the better visual sense and the higher overall accuracy (over 92% for the test image Tripoli-1 and approximately 91% for the test image Tripoli-2) of the experimental results together deliver the excellent performance and robustness of the proposed shadow detection approach for shadow detection of high-resolution multispectral satellite remote sensing images. The proposed shadow detection approach is promised to further alleviate typical shadow detection problems of high small shadow omission and typical nonshadow misclassification for high-resolution multispectral satellite remote sensing images.

Numerous applications are hindered by shadows in high resolution satellite remote sensing images, like image classification, target recognition and change detection. In order to improve remote sensing image utilization, significant importance appears for restoring surface feature information under shadow regions. Problems inevitably occur for current shadow compensation methods in processing high resolution multispectral satellite remote sensing images, such as color distortion of compensated shadow and interference of non-shadow. In this study, to further settle these problems, we analyzed the surface irradiance of both shadow and non-shadow areas based on a satellite sensor imaging mechanism and radiative transfer theory, and finally develop an irradiance restoration based (IRB) shadow compensation approach under the assumption that the shadow area owns the same irradiance to the nearby non-shadow area containing the same type features. To validate the performance of the proposed IRB approach for shadow compensation, we tested numerous images of WorldView-2 and WorldView-3 acquired at different sites and times. We particularly evaluated the shadow compensation performance of the proposed IRB approach by qualitative visual sense comparison and quantitative assessment with two WorldView-3 test images of Tripoli, Libya. The resulting images automatically produced by our IRB method deliver a good visual sense and relatively low relative root mean square error (rRMSE) values. Experimental results show that the proposed IRB shadow compensation approach can not only compensate information of surface features in shadow areas both effectively and automatically, but can also well preserve information of objects in non-shadow regions for high resolution multispectral satellite remote sensing images.

The digital time delay integration (digital TDI) technology of the complementary metal-oxide-semiconductor (CMOS) image sensor has been widely adopted and developed in the optical remote sensing field. However, the details of targets that have low illumination or low contrast in scenarios of high contrast are often drowned out because of the superposition of multi-stage images in digital domain multiplies the read noise and the dark noise, thus limiting the imaging dynamic range. Through an in-depth analysis of the information transfer model of digital TDI, this paper attempts to explore effective ways to overcome this issue. Based on the evaluation and analysis of multi-stage images, the entropy-maximized adaptive histogram equalization (EMAHE) algorithm is proposed to improve the ability of images to express the details of dark or low-contrast targets. Furthermore, in this paper, an image fusion method is utilized based on gradient pyramid decomposition and entropy weighting of different TDI stage images, which can improve the detection ability of the digital TDI CMOS for complex scenes with high contrast, and obtain images that are suitable for recognition by the human eye. The experimental results show that the proposed methods can effectively improve the high-dynamic-range imaging (HDRI) capability of the digital TDI CMOS. The obtained images have greater entropy and average gradients.

The research on hyperspectral anomaly detection algorithms has become a hotspot, driven by a lot of practical applications, such as mineral exploration, environmental monitoring and the national defense force. However, most existing hyperspectral anomaly detectors are designed with a single pixel as unit, which may not make full use of the spatial and spectral information in the hyperspectral image to detect anomalies. In this paper, to fully combine and utilize the spatial and spectral information of hyperspectral images, we propose a novel spectral-based selective searching method for hyperspectral anomaly detection, which firstly combines adjacent pixels with the same spectral characteristics into regions with adaptive shape and size and then treats those regions as one processing unit. Then, by fusing adjacent regions with similar spectral characteristics, the anomaly can be successfully distinguished from background. Two standard hyperspectral datasets are introduced to verify the feasibility and effectiveness of the proposed method. The detection performance is depicted by intuitive detection images, receiver operating characteristic curves and area under curve values. Comparing the results of the proposed method with five popular and state-of-the-art methods proves that the spectral-based selective searching method is an accurate and effective method to detect anomalies.

Low-light image enhancement is a crucial preprocessing task in complex vision tasks. It directly impacts object detection, image segmentation, and image recognition outcomes. In recent years, with the continuous development of deep learning techniques, an increasing number of image enhancement methods based on deep learning have emerged. However, due to the high cost of data collection and the limited content of supervised learning datasets, more and more scholars have shifted their focus to the field of unsupervised image enhancement. Unsupervised image enhancement methods do not require paired images of the same scene during the training process, which greatly reduces the threshold for network training. Nevertheless, current unsupervised methods still suffer from issues such as unstable enhancement effects and limited generalization ability. To address these problems, we propose an improved low-light image enhancement method. The proposed method employs the LSGAN as the training architecture and utilizes an attention map network to dynamically generate attention maps that best fit the network enhancement task, which can effectively improve the generalization ability and enhancement performance of the network. Additionally, we adopt an attention mechanism to enhance the subtle details of the image features. Regarding the network training, considering that the traditional convolutional neural network discriminator may not provide effective guidance to the generator in the early stages of training, we propose an improved discriminator structure. The experimental results demonstrate that our method can achieve good enhancement performance on different datasets and has practical value. Although our method has advantages in enhancing low-light images, it also has certain limitations, such as the network size not meeting the requirements for lightweight models and the potential for further improvement under extremely low-light conditions. We will strive to address these issues as comprehensively as possible in our future research.