Influence of atmospheric modeling on spectral target detection through forward modeling approach in multi-platform remote sensing data

Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality of retrievals are functions of various parameters of the sensor, platform, target–background dynamics, targets’ spectral contrast, and atmospheric influence. Generally, target detection in remote sensing imagery has been approached using various statistical detection algorithms with an assumption of linearity in the image formation process. Knowledge on the image acquisition geometry, and spectral features and their stability across different imaging platforms is vital for designing a spectral target detection system. We carried out an integrated target detection experiment for the detection of various artificial target materials. As part of this work, we acquired a benchmark multi-platform hyperspectral and multispectral remote sensing dataset named as ‘Gudalur Spectral Target Detection (GST-D)’ dataset. Positioning artificial targets on different surface backgrounds, we acquired remote sensing data by terrestrial, airborne, and space-borne sensors on 20th March 2018. Various statistical and subspace detection algorithms were applied on the benchmark dataset for the detection of targets, considering the different sources of reference target spectra, background, and the spectral continuity across the platforms. We validated the detection results using the receiver operation curve (ROC) for different cases of detection algorithms and imaging platforms. Results indicate, for some combinations of algorithms and imaging platforms, consistent detection of specific material targets with a detection rate of about 80% at a false alarm rate between 10−2 to 10−3. Target detection in satellite imagery using reference target spectra from airborne hyperspectral imagery match closely with the satellite imagery derived reference spectra. The ground-based in-situ reference spectra offer a quantifiable detection in airborne or satellite imagery. However, ground-based hyperspectral imagery has also provided an equivalent target detection in the airborne and satellite imagery paving the way for rapid acquisition of reference target spectra. The benchmark dataset generated in this work is a valuable resourcefor addressing intriguing questions in target detection using hyperspectral imagery from a realistic landscape perspective.

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Spectral – spatial urban target detection for hyperspectral remote sensing data using artificial neural network

The Egyptian Journal of Remote Sensing and Space Science ◽

10.1016/j.ejrs.2021.01.002 ◽

2021 ◽

Vol 24 (2) ◽

pp. 173-180

Author(s):

Shalini Gakhar ◽

Kailash Chandra Tiwari

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Target Detection ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Sensing Data ◽

Artificial Neural

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High-Resolution Remotely Sensed Small Target Detection by Imitating Fly Visual Perception Mechanism

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/789429 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Fengchen Huang ◽

Lizhong Xu ◽

Min Li ◽

Min Tang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Visual Perception ◽

Visual System ◽

Target Detection ◽

Hot Spot ◽

Remote Sensing Data ◽

Small Target ◽

Sensing Data ◽

Small Target Detection

The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method.

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Pen Culture Detection Using Filter Tensor Analysis with Multi-Temporal Landsat Imagery

Remote Sensing ◽

10.3390/rs12061018 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1018

Author(s):

Yanxin Xi ◽

Luyan Ji ◽

Xiurui Geng

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Target Detection ◽

Taihu Lake ◽

Landsat Imagery ◽

Remote Sensing Data ◽

Tensor Analysis ◽

Landsat Data ◽

Sensing Data ◽

Multi Temporal

Aquaculture plays an important role in China’s total fisheries production nowadays, and it leads to a few problems, for example water quality degradation, which has damaging effect on the sustainable development of environment. Among the many forms of aquaculture that deteriorate the water quality, disorderly pen culture is especially severe. Pen culture began very early in Yangchenghu Lake and Taihu Lake in China and part of the pen culture still exists. Thus, it is of great significance to evaluate the distribution and area of the pen culture in the two lakes. However, the traditional method for pen culture detection is based on the factual measurement, which is labor and time consuming. At present, with the development of remote sensing technologies, some target detection algorithms for multi/hyper-spectral data have been used in the pen culture detection, but most of them are intended for the single-temporal remote sensing data. Recently, a target detection algorithm called filter tensor analysis (FTA), which is specially designed for multi-temporal remote sensing data, has been reported and has achieved better detection results compared to the traditional single-temporal methods in many cases. This paper mainly aims to investigate the pen culture in Yangchenghu Lake and Taihu Lake with FTA implemented on the multi-temporal Landsat imagery, by determining the optimal time phases combination of the Landsat data in advance. Furthermore, the suitability and superiority of FTA over Constrained Energy Minimization (CEM) in the process of pen culture detection were tested. It was observed in the experiments on the data of those two lakes that FTA can detect the pen culture much more accurately than CEM with Landsat data of selected bands and of limited number of time phases.

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