When it comes to the application of the photon data gathered by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), accurately removing noise is crucial. In particular, conventional denoising algorithms based on local density are susceptible to missing some signal photons when there is uneven signal density distribution, as well as being susceptible to misclassifying noise photons near the signal photons; the application of deep learning remains untapped in this domain as well. To solve these problems, a method for extracting signal photons based on a GoogLeNet model fused with a Convolutional Block Attention Module (CBAM) is proposed. The network model can make good use of the distribution information of each photon’s neighborhood, and simultaneously extract signal photons with different photon densities to avoid misclassification of noise photons. The CBAM enhances the network to focus more on learning the crucial features and improves its discriminative ability. In the experiments, simulation photon data in different signal-to-noise ratios (SNR) levels are utilized to demonstrate the superiority and accuracy of the proposed method. The results from signal extraction using the proposed method in four experimental areas outperform the conventional methods, with overall accuracy exceeding 98%. In the real validation experiments, reference data from four experimental areas are collected, and the elevation of signal photons extracted by the proposed method is proven to be consistent with the reference elevation, with R² exceeding 0.87. Both simulation and real validation experiments demonstrate that the proposed method is effective and accurate for extracting signal photons.

The selection of reasonable locations for urban wastewater treatment plants (WWTPs) is significant in controlling water pollution. However, most current WWTP location selection models focus only on population density, industrial zone scale and geographic elements, while ignoring water pollution and local socio-cultural indicators. We propose a novel indicator system from RS/GIS data to select reasonable locations for WWTPs in Phnom Penh to avoid water environmental deterioration and harm to human health. The frequency of occurrence of water eutrophication is derived from time series RS data and reveals the degree of pollution of local water ecosystems, and is included as a demand indicator for the first time. In order to respect local socio-cultural customs, buffer zones for religious sites are included in the indicator system for the first time to fully determine the zones where construction of WWTPs is forbidden. Subsequently, WWTP locations are selected with the fusion of the minimized facilities number and maximum coverage models. The results demonstrate that the selected locations are all highly suitable and outside zones where construction is forbidden. The method proposed in the article provides a more comprehensive and scientific perspective for WWTP location selection.

Urban change detection (CD) using remote sensing images is of great significance for monitoring and analyzing the spatial and temporal distribution of changes within cities, which can be used to guide urban management. In order to reduce false detections and improve the CD automation, a novel CD framework for high resolution images is proposed in this paper. Firstly, a novel multi-level matching feature is presented, combining structural invariant features with multi-scale dense matching features to comprehensively describe the invariant properties of ground objects at different levels. Secondly, a newly automatic training sample extraction strategy is proposed, in which sufficient and accurate no-change samples can be obtained by Gaussian-weighted Dempster-Shafer evidence theory and L1-norm, meanwhile, typical change samples can be extracted by sequential spectral change vector analysis. Utilizing the automatic extracted samples, the final CD results are obtained using four supervised classifiers respectively (k-nearest neighbor, support vector machine, rotation forest, and extra-trees). To validate the proposed CD framework, experiments are conducted in four datasets with spectral variability, spectral confusion between the changed objects and unchanged backgrounds, and mis-registration. The results demonstrate that the proposed multi-level matching feature and automatic sample extraction strategy can obtain better results with different types of supervised classifiers, and can effectively improve automation of CD, which is applicable to the large spatial extent scene.