Selection of IRS-P6 LISS-4 MO mode band for producing band-sharpened multispectral imagery

The recently launched IRS-P6 satellite has a unique capability of acquiring simultaneously multispectral data at three different spatial resolutions from three independent optical sensors (LISS-4, LISS-3 and AWIFS). Of these, the LISS-4 sensor can be operated in two modes: (i) multispectral (MX) mode covering a swath of 23 km and (ii) monochromatic (MO) mode covering a 70-km swath, both at a spatial resolution of 5 m. One of the important uses of the LISS-4 MO data is in realizing a 5 m band-sharpened multispectral image by merging it with the low-resolution LISS-3 MS image. Operationally anyone of the three LISS-4 bands can be chosen for the MO mode data acquisition. The performance of each band for producing band-sharpened MS images is evaluated, and the choice of the band based on the spatial and spectral characteristics of the merged data is suggested. The LISS-4 Red-band is found to be optimal. It provides band-sharpened imagery with spatial and spectral qualities very similar to the LISS-4 MX data products.     

Atmospheric correction to IRS-P6 AWiFS data and its validation with ground measurements: A study over the semi-arid region

In this study, we have implemented a fast atmospheric correction algorithm to IRS-P6 advanced wide field sensor (AWiFS) satellite data for retrieving surface reflectance under different atmospheric and surface conditions. The algorithm is based on MODIS climatology products and simplified use of Second Simulation of Satellite Signal in Solar Spectrum (6S) radiative transfer code. The algorithm requires information on aerosol optical depth (AOD) for correcting the satellite dataset. The atmospheric correction algorithm has been tested for IRS-P6 AWiFS False colour composites covering the International Crops Research Institute for the Semi-Arid Tropics Farm, Patancheru, Hyderabad, India, under varying atmospheric conditions. Ground measurements of surface reflectance representing different land use/land cover, i.e. red soil, chick pea, groundnut and pigeon pea crops were conducted to validate the algorithm. Terra MODIS AOD₅₅₀ validated with Microtops-II sun photometer–derived AOD₅₀₀ over the urban region of Hyderabad exhibited very good correlation of ～0.92, suggesting possible use of satellite-derived AOD for atmospheric correction.     

近海卫星测高波形分类与判别

针对近海测高数据可用率低下以及传统重跟踪技术的局限性,该文提出了基于动态聚类分析的测高波形分类方法以及基于移动最小距离的波形判别方法,并给出了确定最佳分类数的有效性指标。实现了海洋波形与非海洋波形正确识别,为处理海量测高波形以及根据波形研究最佳重跟踪算法提供了便利。以穿过台湾海峡和台湾近岸的TOPEX/Poseidon波形数据为例进行试验,结果表明,测高波形分为海洋波形、尖锥波形和其他散射波形3类时聚类效果最佳。与OCOG、移动相关系数和β-5等方法的分类结果对比分析表明,本文提出的方法具有一定优势。     

Dry-season vegetation mass and cover fraction from SWIR1.6 and SWIR2.1 band ratio: Ground-radiometer and MODIS data in the Sahel

The potential of the short-wave infrared (SWIR) bands to detect dry-season vegetation mass and cover fraction is investigated with ground radiometry and MODIS data, confronted to vegetation data collected in rangeland and cropland sites in the Sahel (Senegal, Niger, Mali). The ratio of the 1.6 and 2.1 μm bands (called STI) acquired with a ground radiometer proved well suited for grassland mass estimation up to 2500 kg/ha with a linear relation (r² = 0.89). A curvilinear regression is accurate for masses ranging up to 3500 kg/ha. STI proved also well suited to retrieve vegetation cover fraction in crop fields, fallows and rangelands. Such dry-season monitoring, with either ground or satellite data, has important applications for forage, erosion risk and fire risk assessment in semi-arid areas.     

面向对象与卷积神经网络模型的GF-6 WFV影像作物分类

GF-6WFV影像是中国首颗带有红边波段的中高分辨率8波段多光谱卫星的遥感影像,对于其影像及红边波段对作物分类影响的研究利用亟待展开.本文结合面向对象和深度学习提出一种适用于GF-6 WFV红边波段的卷积神经网络(RE-CNN)遥感影像作物分类方法.首先采用多尺度分割和ESP工具选择最佳分割参数完成影像分割,通过面向对...     

Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach

An attempt has been made to understand the potential of temporal Advanced Wide Field Sensor (AWiFS) data aboard IRS-P6 (Resourcesat) to generate the land use land cover information along with the net sown area. The temporal data sets were georeferenced, converted to top of atmosphere reflectance and classified using decision tree classifier, See5. Results indicate that the temporal data set could give a better definition of training sites thereby resulting in good overall kappa (kappa = 0.8651) as well as individual classification accuracies. However, co-registration of temporal datasets accuracies also has got a significant influence on the classification accuracy. Temporal variation in cloud infestation and availability of appropriate data sets within the season (before harvest of the crop) has also affected the classification accuracy.     

IRS-P5卫星影像立体制图精度的研究

本文研究了IRS-P5卫星立体影像制图的精度问题,通过实验分析得出每个立体像对只需要5个地面控制点即可达到最好的平面和高程精度,基本满足铁路1∶10000地形图的精度要求,而过多的地面控制点对于精度的提高没有实质影响。本文的研究成果为利用IRS-P5卫星立体影像制图地面控制测量提供了一定的参考依据。     

Utilization of Resourcesat-1 data for improved crop discrimination

Subsequent to the launch of the state-of-art third generation Indian Remote Sensing satellite, Resourcesat-1, studies have been conducted to understand the capabilities of the on-board sensors for crop discrimination. The paper discusses the unique capabilities of the AWiFS, LISS-III and LISS-IV sensors in terms of their dimensionality, radiometry and spatial resolutions for crop discrimination and monitoring. The studies have indicated better crop discriminability especially using the short wave infrared data in 1.55–1.70 μm data among the spectrally confusing land cover classes, attributed to the relative differences of water contents. 10-bit radiometry of AWiFS data in four bands has been observed to be a better discriminant. Intrafield variability was very well captured by the LISS-IV data revealing the potential of data for applications like precision farming. The studies have revealed that potential of Resourcesat-1 data becoming the workhorse for several agricultural applications.     

Study of crop discrimination by simulated IRS 1C LISS-III data

A study on crop discrimination was carried out using simulated IRS 1C LISS-III data produced using visible (to simulate B2, B3) and NIR (to simulate B4) channels from SPOT and middle infrared (MIR) channel (to simulate B5) from TM over a previously investigated test site, characterized by multiple crops and small fields, in Sabarkantha district (Gujarat). The separability amongst dominant kharif season crops, namely, cotton, groundnut, maize, pigeonpea, between crops and various natural vegetation classes was investigated using Jeffries-Matusita (JM) distance, a pair-wise inter-class separability measure. The study highlighted the capability of simulated LISS-III data to be useful in identifying and labelling small fields and the 4-band data set (B2345, i.e., simulated LISS-III) to significantly improve the separability amongst various crops and vegetation over two 3-band sets (B234, equivalent to SPOT)and B345.     

基于IRS-P6卫星影像的高原地貌分类与信息提取

利用遥感影像进行地形地貌制图,因其成图周期短、制图精度高、修改方便和更新快捷等优势,现已成为中小比例尺地貌制图的主要方法。利用高分辨率的IRS-P6卫星影像对青海省天峻县的高原地貌进行分类与信息提取,并制作大比例尺地貌图。采用遥感(remote sensing,RS)和GIS技术对遥感数据和不同来源的辅助数据进行处理,将其统一到同一GIS平台,在此基础上详细阐述了地貌分类、信息提取的技术流程和地貌图制作的方法。研究表明,基于高分辨率遥感影像数据,采用RS和GIS相结合的技术,可以大大降低地貌分类的难度,提高地貌信息提取与制图的速度和效率。     

Terrain Characterization for Soil Resource Mapping Using IRS-P6 Data and GIS - A Case Study From Basaltic Terrain of Central India

In the present study, landforms and soils have been characterized in Borgaon Manju watershed of basaltic terrain located in Akola district, Maharashtra, Central India. Terrain characterization using Shuttle Radar Topography Mission (SRTM) elevation data (90 m) and IRS-P6 LISS IV data in conjunction with adequate field surveys shows nine distinct landforms. Soil resource inventory shows fourteen soil series in the study area. Soils formed on gently sloping (3–8 %) subdued plateau are very shallow (23 cm), moderately well drained, moderate (15–40 %) surface stoniness, severely eroded, clayey and slightly alkaline in reaction, whereas, the soils formed on level to nearly level (0–1 %) slope in the main valley are very deep (>150 cm), well drained, very slight (<3 %) surface stoniness, moderately eroded with clayey surface and moderately alkaline in reaction. Soils in the watershed are grouped into Lithic Ustorthents, Vertic Haplustepts, Calcic Haplustepts, Typic Haplustepts, Typic Haplusterts and Sodic Calciusterts. The study demonstrates that the analysis of SRTM elevation data and IRS P6–IV data in Geographic Information System (GIS) with adequate field surveys helps in characterization of landforms and soils in analysis of landscape-soil relationship.     

Assessing the suitability of data from Sentinel-1A and 2A for crop classification

Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.     

面向土地利用分类的HJ-1 CCD影像最佳分形波段选择

环境一号卫星（HJ-1）CCD影像光谱波段较少,地物之间的准确分类识别有一定困难。采用分形纹理辅助地物分类识别是一种有效方法,而波段选择是提高分类识别精度的关键。本文以江西赣州定南县土地利用分类为例,采用双毯覆盖模型对HJ卫星CCD影像6类典型地物的波谱分形特征进行了分析,利用不同地物在不同波段上的分形区分度差异构建了最佳分形波段选择模型,并利用该模型挑选出最佳分形波段来辅助土地利用分类,最后对分类结果进行检验。结果表明：最佳分形波段选择模型能够综合权衡不同地物在不同波段上的分形区分度差异,利用挑选出来的最佳分形波段来辅助分类,其分类总体精度相对于原始影像分类提高了11.77%,相对于第1主成分分形辅助下的分类提高了1.56%。     

高光谱图像分类的Wasserstein配置熵非监督波段选择方法

高光谱图像波段选择需考虑波段信息.传统香农信息熵指标仅考虑图像的组分信息(像元的种类和比例),忽略了图像的空间配置信息(像元的空间分布),后者可由玻尔兹曼熵刻画.其中,Wasserstein配置熵删除了连续像元的冗余信息,但局限于四邻域,本文将Wasserstein配置熵拓展至八邻域.以印度松木试验场和意大利帕维亚大学...     

Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium

Global time series of low resolution images are available with high repeat frequency and at low cost, but their analysis is hampered by the presence of mixed pixels and the difficulty in locating detailed spatial features. This study examined the potential of sub-pixel classification for regional crop area estimation using time series of monthly NDVI-composites of the 1 km resolution sensor SPOT-VEGETATION. Belgium was selected as test zone, because of the availability of ample reference data in the form of a vectorial GIS with the boundaries and cover type of the large majority of agricultural fields. Two different methods were investigated: the linear mixture model and neural networks. Both result in area fraction images (AFIs), which contain for each 1 km pixel the estimated area proportions occupied by the different cover types (crops or other land use). Both algorithms were trained with part of the reference data and validated with the remainder. Validation was repeated at three different levels: the 1 km pixel, the municipality and the agro-statistical district. In general, the neural network outperformed the linear mixture model. For the major classes (winter wheat, maize, forest) the obtained acreage estimates showed good agreement with the true values, especially when aggregated to the level of the municipality (R² ≈ 85%) or district (R² ≈ 95%). The method seems attractive for wide-scale, regional area estimation in data-poor countries.     

A new method for retrieving band 6 of aqua MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key research instrument for the NASA Earth Observing System (EOS) mission. It was successfully launched onboard the Terra satellite in December 1999 and Aqua satellite in May 2002. Both MODIS instruments have been working well except that 15 of the 20 detectors in Aqua MODIS band 6 (1.628-1.652 /spl mu/m) are either nonfunctional or noisy. The striping in Aqua MODIS band 6 caused by its nonfunctional or noisy detectors has been a serious problem for MODIS snow products, which use band 6 primarily for snow detection. MODIS scientists have been using Aqua MODIS band 7 (2.105-2.155 /spl mu/m) instead of band 6 for computing the normalized difference snow index to continue detecting global snow coverage. The main objective of this letter is to retrieve Aqua MODIS band 6 using other bands based on their relationships in Terra MODIS. The band retrieval approach proposed in this letter is also very useful for the next generation of MODIS sensor-the Visible/Infrared Imager/Radiometer Suite (VIIRS) band M10 proxy data generation. Such proxy data can support the VIIRS prelaunch end-to-end testing, postlaunch calibration/validation, and data quality checking.     

An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data

The operational land imager (OLI) is the latest instrument in the Landsat series of satellite imagery, which officially began normal operations on 30 May 2013. The OLI includes two bands that are not on the thematic mapper series of sensors aboard Landsat-5 and 7; a cirrus band and a coastal/aerosol band. This paper compares the classification and regression tree and the kernel-based extreme learning machine (KELM) for mapping crops in Hokkaido, Japan, using OLI data, except the cirrus band and the pan band. The OLI data acquired on 8 July 2013 was used for crop classification of beans, beets, grassland, maize, potatoes and winter wheat. The KELM algorithm performed better in this study and achieved overall accuracies of 90.1%. According to the Jeffries–Matusita (J–M) distances, the short wavelength infrared band provides the greater contribution (the highest value was observed for band 6 in OLI data).     

Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach

In this study, temporal MODIS-Terra MOD13Q1 data have been used for identification of wheat crop uniquely, using the noise clustering (NC) soft classification approach. This research also optimises the selection of date combination and vegetation index for classification of wheat crop. First, a separability analysis is used to optimise the date combination for each case of number of dates and vegetation index. Then, these scenes have undergone for NC soft classification. The resolution parameter (δ) was optimised for the NC classifier and found to be a value of 1.6 × 10⁴ for wheat crop identification. Classified outputs were analysed by receiver operating characteristics (ROC) analysis for sub-pixel detection. Highest area under the ROC curve was found for soil-adjusted vegetation index corresponding to the three different phenological stages data sets. From this study, the data sets corresponding to the Sowing, Flowering and Maturity phenological stages of wheat crop were found more suitable to identify it uniquely.     

基于遥感和GIS的区域生态环境分类研究

本文以石羊河流域为例,研究了基于卫星遥感数据与数字高程模型的区域生态环境分类方法。其研究 机理是:利用遥感技术获取相关生态环境专题信息,运用GIS空间分析技术自动生成流域范围的生态环境边界, 形成了一种科学的区域生态环境分类方法。