ENVI .HDR + .DAT
Apr 28,2026

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Introduction

ENVI .HDR + .DAT is a raster data format widely used in ENVI, a remote sensing analysis software. It is primarily designed for storing multispectral images such as satellite imagery, aerial photographs, and hyperspectral data. This format consists of a combination of a .DAT file that stores the actual pixel data and a .HDR header file that stores metadata such as image size, number of bands, data type, and coordinate system. The ENVI format is simple in structure yet highly flexible, and is widely used in the fields of remote sensing and geospatial analysis. Additionally, due to its near-open specification, it can be read by many GIS and remote sensing software applications.

File Structure

ENVI raster data typically consists of at least two files: .HDR and .DAT, which together form a complete dataset. The main components are as follows:

.hdr (Header file): A text file that stores raster data metadata. It describes basic information such as the number of samples (columns), lines (rows), bands, data type (integer, floating point, etc.), and interleaving method (BSQ, BIL, BIP). It can also include coordinate systems and georeferencing information.
.dat (Data file): A binary file that stores the actual raster pixel data. Pixel values are stored in sequence according to the data structure defined in the HDR file. For multi-band data, pixels are arranged by band, by line, or by pixel depending on the interleaving method.
.aux.xml (Auxiliary metadata file, optional): May be generated by GIS software such as GDAL to store additional coordinate information and statistics.
.prj (Projection information file, optional): A file that stores the geographic or projected coordinate system (e.g., WGS84 or UTM) of the data. If not described in the HDR file, this file is referenced.

Interleaving settings in .hdr: Specifies the storage method of image data.

BSQ (Band Sequential): Stores data band by band
BIL (Band Interleaved by Line): Alternates bands line by line
BIP (Band Interleaved by Pixel): Alternates bands pixel by pixel

Pros

High adaptability to multi-band data: Suitable for storing remote sensing data with many bands, such as hyperspectral and multispectral images.
Simple and flexible structure: Since the HDR file is in text format, the data structure is easy to check and edit, making it convenient for research and data analysis.
Supports multiple interleaving methods: Storage methods such as BSQ, BIL, and BIP enable efficient data access depending on the analysis application.
Broad software compatibility: Can be read not only by ENVI but also by many GIS and remote sensing software applications as well as the GDAL library.
Handles large data volumes: The binary format allows efficient storage of large volumes of data such as satellite images and high-resolution raster data.

Cons

Management complexity due to file separation: Since both HDR and DAT files are required for correct data loading, careful file management is necessary.
Compression not standard: Compared to formats like GeoTIFF, there is no standard compression method, which can result in large file sizes.
Limited spatial metadata: Depending on the content described in the HDR file, coordinate systems and georeferencing information may be insufficient.
Limited GIS functionality: Compared to dedicated GIS formats, there are functional limitations in attribute tables and advanced metadata management.
Lack of pyramids or tile structures: Multi-resolution structures for display optimization are not standard, so additional processing may be required for fast display of large images.

Application Scenario

The ENVI .HDR + .DAT format is primarily used in remote sensing and geospatial analysis. It is suitable for storing and analyzing satellite images, aerial photographs, and hyperspectral data, and is widely used in research on vegetation index calculation, land cover classification, and environmental monitoring. As a data format suitable for remote sensing-specific analysis workflows such as multi-band image processing and spectral analysis, it plays an important role in fields such as academic research, environmental monitoring, agricultural analysis, and resource exploration.