In complex environments, the clutter statistical characteristics of synthetic aperture radar (SAR) are inconstant, and the constant detection performance of a false alarm rate (CFAR) detector based on a clutter statistical model is also hard to achieve. As a result, the overestimated threshold leads to a degradation in detection probability. To this end, this paper proposes a SAR ship detector different from CFAR detectors, which is independent of traditional clutter statistical distribution models and the probability of a false alarm (PFA). The proposed detector aims to raise the ship detection probability and alleviate interference from complex environments such as multi-target areas, shores, and breakwaters. It estimates clutter-truncated thresholds based on clutter intensity statistics (CIS). Firstly, three statistical parameters, including the mean, standard deviation, and maximum intensity of background clutter contaminated by outliers, are calculated; secondly, these parameters are utilized to estimate the clutter-truncated threshold using the novel CIS; and finally, the pixel under test is determined according to the CIS detection rule. Compared with CFAR-based algorithms, CIS obtains a high probability of detection in complex environments. As for other aspects, the CIS detector is insensitive to the structure of the detection window, as well as the size. It is also computationally efficient due to its simple calculations. The superiority of the CIS detector is validated on scene-differed SAR images from the DSSDD dataset.

Signal-to-Noise Ratio (SNR) is the benchmark to evaluate the quality of optical remote sensors. For SNR estimation, most of the traditional methods have complicated processes, low efficiency, and general accuracy. In particular, they are not suitable for the distributed computation on intelligent satellites. Therefore, an intelligent SNR estimation algorithm with strong computing power and more accuracy is urgently needed. Considering the simplicity of distributed deployment and the lightweight goal, our first proposition is to design a convolutional neural network (CNN) similar to VGG (proposed by Visual Geometry Group) to estimate SNR for optical remote sensors. In addition, considering the advantages of multi-branch structures, the second proposition is to train the CNN in a novel method of multi-branch training and parameter-reconstructed inference. In this study, simulated and real remote sensing images with different ground features are utilized to validate the effectiveness of our model and the novel training method. The experimental results show that the novel training method enhances the fitting ability of the network, and the proposed CNN trained in this method has high accuracy and reliable SNR estimation, which achieves a 3.9% RMSE for noise-level-known simulated images. When compared to the accuracy of the reference methods, such as the traditional and typical SNR methods and the denoising convolutional neural network (DnCNN), the performance of the proposed CNN trained in a novel method is the best, which achieves a relative error of 5.5% for hyperspectral images. The study is fit for optical remote sensing images with complicated ground surfaces and different noise levels captured by different optical remote sensors.